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Roadmap of LlamaIndex to creating personalized Q&A chatbots
Zaid Ahmed
| September 28, 2023

LlamaIndex is an orchestration framework for large language model (LLM) applications. LLMs like GPT-4 are pre-trained on massive public datasets, allowing for incredible natural language processing capabilities out of the box. However, their utility is limited without access to your own private or domain-specific data. 

LlamaIndex solves this problem by providing a way to ingest, structure, and access your own data for use with LLMs. It supports a variety of data sources, including APIs, databases, and PDFs.

Once your data is indexed, it provides a number of ways to interact with it, including: 

  • Natural language querying: You can ask LlamaIndex questions about your data in plain English. For example, you could ask “What are the top 10 revenue-generating products?” or “What are the most common customer complaints?” 
  • Conversation with LLM-powered data agents: It can be used to create chatbots or other conversational interfaces that can access and process your data in real-time. This allows you to build applications that can provide personalized assistance to your users or answer their questions in a comprehensive and informative way. 
  • LLM-powered data analytics: It can also be used to power LLM-based data analytics applications. For example, you could use it to build a system that can automatically generate reports or insights from your data. 

 

Learn to build LLM applications                                          

Key components of LlamaIndex: 

The key components of LlamaIndex are as follows:  

  • Data connectors: These components allow LlamaIndex to ingest data from a variety of sources, such as APIs, databases, and PDFs. The data is converted into a simple document format that is easy for LlamaIndex to process. 
  • Data index: A data structure that stores the data in a way that makes it easy for LlamaIndex to find the relevant information when a user asks a question or starts a conversation. 
  • Retrievers: Retrievers are responsible for finding the most relevant information in the data index based on the user’s query or chat message. 
  • Query engines: Allow users to ask questions about their data in natural language. They accept natural language queries and provide comprehensive and informative responses. 
  • Chat engines: Allow users to have interactive conversations with their data. They maintain a contextual understanding of the conversation history and can provide answers that consider the relevant past context. 

 

 

 

In this tutorial, we will delve into the technical intricacies of constructing intelligent chatbots that leverage advanced technologies. Our example code will illustrate the development of a PDF Q&A chatbot that incorporates the OpenAI language model, VectorStoreIndex for document indexing and Streamlit for user interface design.

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Furthermore, the chatbot will be equipped with the Llama Index’s Conversational Retrieval Chain, enabling it to furnish precise responses based on user queries. Let’s embark on this journey into the technical aspects of crafting a highly capable chatbot. 

Importing necessary libraries  

To commence our chatbot project, we need to import crucial libraries and functions. Here’s a breakdown of the libraries we will be utilizing: 

  • LlamaIndex: We harness the power of the Llama Index, a comprehensive framework tailored for developing applications enriched by language models. 
  • Streamlit: Streamlit, a Python library, serves as our toolkit for swiftly constructing web applications with an intuitive interface that facilitates user interaction. 

Streamlit 

Setting OpenAI API key  

To access OpenAI’s language models effectively, it is imperative to configure our API key. Replace the placeholder with your actual OpenAI API key, obtainable from the OpenAI API platform. This key will act as our gateway to the powerful language models offered by OpenAI. Also you can use the dotenv route where you place your OPENAI key in the .env file. 

OpenAPIKey

Setting up the user interface: 

This section delves into the creation of our user interface using Streamlit. The interface is meticulously designed to be clean, user-friendly, and feature-rich. It encompasses a title and a minimalist sidebar, providing an entry point for users to engage with our Q&A chatbot seamlessly.
 

user interface

 

 

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Main function and data loading: 

At the core of our chatbot lies the main function, which orchestrates the entire application logic. We initiate the process by loading data from a specified directory using a SimpleDirectoryReader. This data will serve as the knowledge repository from which our chatbot will draw answers to user inquiries. 

Data loading

 

Creating a service context: 

To enable the advanced natural language processing capabilities of our chatbot, we established a ServiceContext. This context is pre-configured with default settings and an OpenAI language model (llm). It lays the groundwork for our chatbot’s ability to understand and generate responses to user queries effectively. 

service context

 

 

 

 

Building the LlamaIndex: 

The pivotal component of our chatbot’s capabilities is the Llama Index. We construct this index using VectorStoreIndex, a versatile tool that optimizes the stored documents for efficient searching. This step ensures that our chatbot can rapidly retrieve pertinent information when faced with user queries. 

vector store index

 

User input and chat engine: 

Our user interface empowers users to input questions related to the provided data through a text input field. The chat engine processes these queries by harnessing the capabilities of the Llama Index. Subsequently, it generates responses based on the content indexed from the documents. This interaction constitutes the core functionality of our Q&A chatbot. 

 

User input

 

Running the application: 

With all the components in place, we culminate our code by executing the main function. This pivotal step transforms our project into an interactive chatbot. Users can seamlessly pose questions, and the chatbot, equipped with the Llama Index, responds with precise answers drawn from the indexed documents. 

Running the application

 

 

Benefits of using LlamaIndex 

There are a number of benefits to using LlamaIndex to create custom LLM applications: 

  • It is easy to use: Provides a simple and intuitive API for interacting with your data. 
  • It is flexible: Supports a variety of data sources and formats. It also provides a number of plugins and integrations that can be used to extend its functionality. 
  • It is scalable: Scaled to handle large datasets and high traffic volumes. 

In conclusion, this guide has offered a comprehensive roadmap for creating personalized Q&A chatbots with the Llama Index at their core.

By integrating cutting-edge technologies such as OpenAI for language processing, VectorStoreIndex for efficient document indexing, and the Llama Index’s Conversational Retrieval Chain, we have unlocked the potential for engaging, informative, and highly interactive question-answering experiences.

Feel encouraged to explore and expand upon this chatbot project, extending its capabilities to tackle more intricate tasks and challenges within the realm of AI-driven conversational systems. 

How to tune LLM Parameters for optimal performance
Ayesha Saleem
| September 11, 2023

Shape your model performance using LLM parameters.

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NOOR, the new largest NLP Arabic language model
Data Science Dojo Staff
| August 31, 2023

Approximately 313 million people speak Arabic, making it the fifth most-spoken language globally.

The United Arab Emirates (UAE) has made significant strides in the field of artificial intelligence and language technology by launching a large Arabic language model. This development involves the creation of advanced AI software, such as Jais, an open-source Arabic Large Language Model (LLM) with high-quality capabilities.

This initiative, driven by organizations like G42 and the Technology Innovation Institute (TII), aims to lead the Gulf region’s adoption of generative AI and elevate Arabic language processing in AI applications. The UAE’s commitment to developing cutting-edge technology like NOOR and Falcon demonstrates its determination to be a global leader in the field of AI and natural language processing.

 

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This initiative addresses the gap in the availability of advanced language models for Arabic speakers. Jais incorporates cutting-edge features such as ALiBi position embeddings, enabling it to handle longer inputs for better context handling and accuracy. The launch of Jais contributes to the acceleration of innovation in the Arab world by providing high-quality Arabic language capabilities for AI applications.

 

Learn the top 20 technical terms in the LLM vicinity

 

Jaison is associated with G42, a company subsidiary of Inception, which has released an open-source AI model named “Jais,” an advanced Arabic Large Language Model (LLM). Jais is a transformer-based large language model designed to cater to the significant user base of Arabic speakers, estimated to be over 400 million.

 

Jais Arabic language model | Data Science Dojo
Source: Reddit

Use-cases for the newly introduced Arabic AI model

The Arabic language models, such as “Jais” and “AraGPT2,” are developed to advance the field of natural language processing and AI technology for the Arabic language. They will be used for various applications, including:

  • Enabling more accurate and efficient text generation and understanding in Arabic.
  • Enhancing communication and engagement between Arabic-speaking users and AI systems.
  • Facilitating language translation, sentiment analysis, and information extraction in Arabic content.
  • Boosting the development of AI-driven applications in fields like education, customer service, content creation, and more.
  • Expanding the accessibility of advanced AI technologies to the Arabic-speaking community.
  • Fostering innovation and research in Arabic language processing, contributing to the growth of AI in the Arab world.

These language models aim to bridge the gap in AI technology for Arabic speakers and empower a wide range of industries with improved language-related capabilities.

 

UAE businesses leveraging the Arabic language model

Businesses in the UAE can benefit from Arabic language models in several ways:

  • Enhanced Communication: Arabic language models enable businesses to communicate more effectively with Arabic-speaking customers, fostering better engagement and customer satisfaction.
  • Localized Content: Businesses can create localized marketing campaigns, advertisements, and content that resonates with the local audience, improving brand perception.
  • Customer Support: AI-powered chatbots and customer support systems can be developed in Arabic, providing immediate assistance to customers in their native language.
  • Content Generation: Arabic language models can assist in generating high-quality content in Arabic, from articles to social media posts, saving time and resources.
  • Data Analysis: Businesses can analyze Arabic-language data to gain insights into customer preferences, market trends, and sentiment, enabling informed decision-making.
  • Innovation: Arabic language models can fuel innovation in various sectors, from healthcare to finance, by providing advanced AI capabilities tailored to the local context.
  • Efficient Translation: Enterprises dealing with multilingual operations can benefit from accurate and efficient translation services for documents, contracts, and communication.
  • Educational Resources: Arabic language models can aid in developing educational resources, online courses, and e-learning platforms to cater to Arabic-speaking learners.

By leveraging Arabic language models like “Jais,” businesses can tap into the vast potential of AI to enhance their operations, communication, and growth strategies in the UAE and beyond.

Learn to build LLM applications                                          

Busting myths and facts of prompt engineering
Ayesha Saleem
| August 21, 2023

Prompt engineering is the process of designing and refining prompts that are given to large language models (LLMs) to get them to generate the desired output.

The beginning of prompt engineering

The history of prompt engineering can be traced back to the early days of artificial intelligence when researchers were experimenting with ways to get computers to understand and respond to natural language.

Learn in detail about —> Prompt Engineering

Best practices for prompt engineering
Best practices for prompt engineering

One of the earliest examples of prompt engineering was the work of Terry Winograd in the 1970s. Winograd developed a system called SHRDLU that could answer questions about a simple block world. SHRDLU was able to do this by using a set of prompts that were designed to help it understand the context of the question.

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In the 1980s, prompt engineering became more sophisticated as researchers developed new techniques for training LLMs. One of the most important techniques was backpropagation, which allowed Large Language Models to learn from their mistakes. This made it possible to train LLMs on much larger datasets, leading to significant performance improvements.

In the 2010s, the development of deep learning led to a new wave of progress in prompt engineering. Deep learning models are able to learn much more complex relationships between words than previous models. This has made it possible to create prompts that are much more effective at controlling the output of LLMs.

Today, prompt engineering is a critical tool for researchers and developers who are working with LLMs. It is used in a wide variety of applications, including machine translation, text summarization, and creative writing.

Myths vs facts in prompt engineering

Have you tried any of these fun prompts?

  • In the field of machine translation, one researcher tried to get an LLM to translate the phrase “I am a large language model” into French. The LLM responded with “Je suis un grand modèle linguistique”, which is a grammatically correct translation, but it also happens to be the name of a popular French cheese.
  • In the field of text summarization, one researcher tried to get an LLM to summarize the plot of the movie “The Shawshank Redemption”. The LLM responded with a summary that was surprisingly accurate, but it also included a number of jokes and puns.
  • In the field of creative writing, one researcher tried to get an LLM to write a poem about a cat. The LLM responded with a poem that was both funny and touching.

These are just a few examples of the many funny prompts that people have tried with LLMs. As LLMs become more powerful, it is likely that we will see even more creative and entertaining uses of prompt engineering.

Want to improve your prompting skills? Click below:

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Some unknown facts about Prompt Engineering

  • It is a relatively new field, and there is still much that we do not know about it. However, it is a rapidly growing field, and there are many exciting new developments happening all the time.
  • The effectiveness of a prompt can depend on a number of factors, including the specific LLM being used, the training data that the LLM has been trained in, and the context in which the prompt is being used.
  • There are a number of different techniques that can be used for prompt engineering, and the best technique to use will depend on the specific application.
  • It can be used to control a wide variety of aspects of the output of an LLM, including the length, style, and content of the output.
  • It can be used to generate creative and interesting text, as well as to solve complex problems.
  • It is a powerful tool that can be used to unlock the full potential of LLMs.

 

Learn how to become a prompt engineer in 10 steps 

10 steps to become a prompt engineer
10 steps to become a prompt engineer

Here are some specific examples of important and unknown facts about prompting:

  • It is possible to use prompts to control the creativity of an LLM. For example, one study found that adding the phrase “in a creative way” to a prompt led to more creative outputs from the LLM.
  • Prompts can be used to generate text that is consistent with a particular style. For example, one study found that adding the phrase “in the style of Shakespeare” to a prompt led to outputs that were more Shakespearean in style.
  • Prompts can be used to solve complex problems. For example, one study found that adding the phrase “prove that” to a prompt led to the LLM generating mathematical proofs.
  • It is a complex and challenging task. There is no one-size-fits-all approach to prompt engineering, and the best way to create effective prompts will vary depending on the specific application.
  • It is a rapidly evolving field. There are new developments happening all the time, and the field is constantly growing and changing.

Most popular myths and facts of prompt engineering

In this ever-evolving realm, it’s crucial to discern fact from fiction to stay ahead of the curve. Our team of experts has meticulously sifted through the noise to present you with the most accurate insights, dispelling myths that might have clouded your understanding. Let’s delve into the heart of prompting and uncover the truths that can drive your success.

Myth: Prompt engineering is just about keywords

Fact: Prompt engineering is a symphony of elements

Gone are the days when prompt engineering was solely about sprinkling keywords like confetti. Today, it’s a meticulous symphony of various components working harmoniously. While keywords remain pivotal, they’re just one part of the grand orchestra. Structured data, user intent analysis, and contextual relevance are the unsung heroes that make your prompt engineering soar. Balancing these elements crafts a narrative that resonates with both users and search engines.

Myth: More prompts, higher results

Fact: Quality over quantity

Quantity might impress at first glance, but it’s quality that truly wields power in the world of prompt engineering. Crafting a handful of compelling, highly relevant prompts that align seamlessly with your content yields far superior results than flooding your page with irrelevant ones. Remember, it’s the value you provide that keeps users engaged, not the sheer number of prompts you throw their way.

Myth: Prompt engineering is a one-time task

Fact: Ongoing optimization is the key

Imagine your website as a garden that requires constant tending. Similarly, prompt engineering demands continuous attention. Regularly analyzing the performance of your prompts and adapting to shifting trends is paramount. This ensures that your content remains evergreen and resonates with the dynamic preferences of your audience.

Myth: Creativity has no place in prompt engineering

Fact: Creativity elevates engagement

While prompt engineering involves a systematic approach, creativity is the secret ingredient that adds flavor to the mix. Crafting prompts that spark curiosity, evoke emotion, or present a unique perspective can exponentially boost user engagement. Metaphors, analogies, and storytelling are potent tools that, when woven into your prompts, make your content unforgettable.

Myth: Only text prompts matter

Fact: Diversify with various formats

Text prompts are undeniably significant, but limiting yourself to them is a missed opportunity. Embrace a diverse range of prompt formats to cater to different learning styles and preferences.

Visual prompts, such as infographics and videos, engage visual learners, while audio prompts cater to those who prefer auditory learning. The more versatile your prompt formats, the broader your audience reaches.

Myth: Prompt engineering and SEO are unrelated

Fact: Symbiotic relationship

Prompt engineering and SEO are not isolated islands; they’re interconnected domains that thrive on collaboration. Solid prompt engineering bolsters SEO by providing search engines with the context they crave. Conversely, a well-optimized website enhances prompt engineering, as it ensures your content is easily discoverable by your target audience.

Myth: Complex language boosts credibility

Fact: Clarity trumps complexity

Using complex jargon might seem like a credibility booster, but it often does more harm than good. Clear, concise prompts that resonate with a broader audience hold more weight. Remember, the goal is not to showcase your vocabulary prowess but to communicate effectively and establish a genuine connection with your readers.

Myth: Prompt engineering is set-and-forget

Fact: Continuous monitoring is vital

Once you’ve orchestrated your prompts, it’s not time to sit back and relax. The digital landscape is in perpetual motion, and so should be your approach to prompt engineering. Monitor the performance of your prompts regularly, employing data analytics to identify patterns and make informed adjustments that keep your content relevant and engaging.

Myth: Only experts can master prompt engineering

Fact: Learning and iteration lead to mastery

While prompt engineering might appear daunting, it’s a skill that can be honed with dedication and a willingness to learn. Don’t shy away from experimentation and iteration. Embrace the insights gained from your data, be open to refining your approach, and gradually you’ll find yourself mastering the art of prompt engineering.

Get on the journey of prompt engineering

Prompt engineering is a dynamic discipline that demands both strategy and creativity. Dispelling these myths and embracing the facts will propel your content to new heights, setting you apart from the competition. Remember, prompt engineering is not a one-size-fits-all solution; it’s an evolving journey of discovery that, when approached with dedication and insight, can yield remarkable results

The 40-hour LLM application roadmap: Learn to build your own LLM applications from scratch
Ayesha Saleem
| August 10, 2023

Large language models (LLMs) are one of the most exciting developments in artificial intelligence. They have the potential to revolutionize a wide range of industries, from healthcare to customer service to education. But in order to realize this potential, we need more people who know how to build and deploy LLM applications.

That’s where this blog comes in. In this blog, we’re going to discuss the importance of learning to build your own LLM application, and we’re going to provide a roadmap for becoming a large language model developer.

Large language model bootcamp

We believe this blog will be a valuable resource for anyone interested in learning more about LLMs and how to build and deploy Large Language Model applications. So, whether you’re a student, a software engineer, or a business leader, we encourage you to read on!

Why do I need to build a custom LLM application?

Here are some of the benefits of learning to build your own LLM application:

  • You’ll be able to create innovative new applications that can solve real-world problems.
  • You’ll be able to use LLMs to improve the efficiency and effectiveness of your existing applications.
  • You’ll be able to gain a competitive edge in your industry.
  • You’ll be able to contribute to the development of this exciting new field of artificial intelligence.

 

Read more —> How to build and deploy custom llm application for your business

 

Roadmap to build custom LLM applications

If you’re interested in learning more about LLMs and how to build and deploy LLM applications, then this blog is for you. We’ll provide you with the information you need to get started on your journey to becoming a large language model developer step by step.

build llm applications

1. Introduction to Generative AI:

Generative AI is a type of artificial intelligence that can create new content, such as text, images, or music. Large language models (LLMs) are a type of generative AI that can generate text that is often indistinguishable from human-written text. In today’s business world, Generative AI is being used in a variety of industries, such as healthcare, marketing, and entertainment.

 

Introduction to Generative AI - LLM Bootcamp Data Science Dojo
Introduction to Generative AI – LLM Bootcamp Data Science Dojo

 

For example, in healthcare, generative AI is being used to develop new drugs and treatments, and to create personalized medical plans for patients. In marketing, generative AI is being used to create personalized advertising campaigns and to generate product descriptions. In entertainment, generative AI is being used to create new forms of art, music, and literature.

 

2. Emerging architectures for LLM applications:

There are a number of emerging architectures for LLM applications, such as Transformer-based models, graph neural networks, and Bayesian models. These architectures are being used to develop new LLM applications in a variety of fields, such as natural language processing, machine translation, and healthcare.

 

Emerging architectures for llm applications - LLM Bootcamp Data Science Dojo
Emerging architectures for llm applications – LLM Bootcamp Data Science Dojo

 

There are a number of emerging architectures for LLM applications, such as Transformer-based models, graph neural networks, and Bayesian models. These architectures are being used to develop new LLM applications in a variety of fields, such as natural language processing, machine translation, and healthcare.

For example, Transformer-based models are being used to develop new machine translation models that can translate text between languages more accurately than ever before. Graph neural networks are being used to develop new fraud detection models that can identify fraudulent transactions more effectively. Bayesian models are being used to develop new medical diagnosis models that can diagnose diseases more accurately.

 

3. Embeddings:

Embeddings are a type of representation that is used to encode words or phrases into a vector space. This allows LLMs to understand the meaning of words and phrases in context.

 

Embeddings
Embeddings – LLM Bootcamp Data Science Dojo

 

Embeddings are used in a variety of LLM applications, such as machine translation, question answering, and text summarization. For example, in machine translation, embeddings are used to represent words and phrases in a way that allows LLMs to understand the meaning of the text in both languages.

In question answering, embeddings are used to represent the question and the answer text in a way that allows LLMs to find the answer to the question. In text summarization, embeddings are used to represent the text in a way that allows LLMs to generate a summary that captures the key points of the text.

 

4. Attention mechanism and transformers:

The attention mechanism is a technique that allows LLMs to focus on specific parts of a sentence when generating text. Transformers are a type of neural network that uses the attention mechanism to achieve state-of-the-art results in natural language processing tasks.

 

Attention mechanism and transformers - LLM
Attention mechanism and transformers – LLM Bootcamp Data Science Dojo

 

The attention mechanism is used in a variety of LLM applications, such as machine translation, question answering, and text summarization. For example, in machine translation, the attention mechanism is used to allow LLMs to focus on the most important parts of the source text when generating the translated text.

In answering the question, the attention mechanism is used to allow LLMs to focus on the most important parts of the question when finding the answer. In text summarization, the attention mechanism is used to allow LLMs to focus on the most important parts of the text when generating the summary.

 

5. Vector databases:

Vector databases are a type of database that stores data in vectors. This allows LLMs to access and process data more efficiently.

 

Vector databases - LLM Bootcamp Data Science Dojo
Vector databases – LLM Bootcamp Data Science Dojo

 

Vector databases are used in a variety of LLM applications, such as machine learning, natural language processing, and recommender systems.

For example, in machine learning, vector databases are used to store the training data for machine learning models. In natural language processing, vector databases are used to store the vocabulary and grammar for natural language processing models. In recommender systems, vector databases are used to store the user preferences for different products and services.

 

6. Semantic search:

Semantic search is a type of search that understands the meaning of the search query and returns results that are relevant to the user’s intent. LLMs can be used to power semantic search engines, which can provide more accurate and relevant results than traditional keyword-based search engines.

Semantic search - LLM Bootcamp Data Science Dojo
Semantic search – LLM Bootcamp Data Science Dojo

Semantic search is used in a variety of industries, such as e-commerce, customer service, and research. For example, in e-commerce, semantic search is used to help users find products that they are interested in, even if they don’t know the exact name of the product.

In customer service, semantic search is used to help customer service representatives find the information they need to answer customer questions quickly and accurately. In research, semantic search is used to help researchers find relevant research papers and datasets.

 

7. Prompt engineering:

Prompt engineering is the process of creating prompts that are used to guide LLMs to generate text that is relevant to the user’s task. Prompts can be used to generate text for a variety of tasks, such as writing different kinds of creative content, translating languages, and answering questions.

 

Prompt engineering - LLM Bootcamp Data Science Dojo
Prompt engineering – LLM Bootcamp Data Science Dojo

 

Prompt engineering is used in a variety of LLM applications, such as creative writing, machine translation, and question answering. For example, in creative writing, prompt engineering is used to help LLMs generate different creative text formats, such as poems, code, scripts, musical pieces, email, letters, etc.

In machine translation, prompt engineering is used to help LLMs translate text between languages more accurately. In answering questions, prompt engineering is used to help LLMs find the answer to a question more accurately.

 

8. Fine-tuning of foundation models:

Foundation models are large language models that are pre-trained on massive datasets. Fine-tuning is the process of adjusting the parameters of a foundation model to make it better at a specific task. Fine-tuning can be used to improve the performance of LLMs on a variety of tasks, such as machine translation, question answering, and text summarization.

 

Fine-tuning of Foundation Models - LLM Bootcamp Data Science Dojo
Fine-tuning of Foundation Models – LLM Bootcamp Data Science Dojo

 

Foundation models are pre-trained on massive datasets. Fine-tuning is the process of adjusting the parameters of a foundation model to make it better at a specific task. Fine-tuning is used to improve the performance of LLMs on a variety of tasks, such as machine translation, question answering, and text summarization.

For example, LLMs can be fine-tuned to translate text between specific languages, to answer questions about specific topics, or to summarize text in a specific style.

 

9. Orchestration frameworks:

Orchestration frameworks are tools that help developers to manage and deploy LLMs. These frameworks can be used to scale LLMs to large datasets and to deploy them to production environments.

 

Orchestration frameworks - LLM Bootcamp Data Science Dojo
Orchestration frameworks – LLM Bootcamp Data Science Dojo

 

Orchestration frameworks are used to manage and deploy LLMs. These frameworks can be used to scale LLMs to large datasets and to deploy them to production environments. For example, orchestration frameworks can be used to manage the training of LLMs, to deploy LLMs to production servers, and to monitor the performance of LLMs

 

10. LangChain:

LangChain is a framework for building LLM applications. It provides a number of features that make it easy to build and deploy LLM applications, such as a pre-trained language model, a prompt engineering library, and an orchestration framework.

 

Langchain - LLM Bootcamp Data Science Dojo
Langchain – LLM Bootcamp Data Science Dojo

 

Overall, LangChain is a powerful and versatile framework that can be used to create a wide variety of LLM-powered applications. If you are looking for a framework that is easy to use, flexible, scalable, and has strong community support, then LangChain is a good option.

11. Autonomous agents:

Autonomous agents are software programs that can act independently to achieve a goal. LLMs can be used to power autonomous agents, which can be used for a variety of tasks, such as customer service, fraud detection, and medical diagnosis.

 

Attention mechanism and transformers - LLM
Attention mechanism and transformers – LLM Bootcamp Data Science Dojo

 

12. LLM Ops:

LLM Ops is the process of managing and operating LLMs. This includes tasks such as monitoring the performance of LLMs, detecting and correcting errors, and upgrading Large Language Models to new versions.

 

LLM Ops - LLM Bootcamp Data Science Dojo
LLM Ops – LLM Bootcamp Data Science Dojo

 

13. Recommended projects:

Recommended projects - LLM Bootcamp Data Science Dojo
Recommended projects – LLM Bootcamp Data Science Dojo

 

There are a number of recommended projects for developers who are interested in learning more about LLMs. These projects include:

  • Chatbots: LLMs can be used to create chatbots that can hold natural conversations with users. This can be used for a variety of purposes, such as customer service, education, and entertainment. For example, the Google Assistant uses LLMs to answer questions, provide directions, and control smart home devices.
  • Text generation: LLMs can be used to generate text, such as news articles, creative writing, and code. This can be used for a variety of purposes, such as marketing, content creation, and software development. For example, the OpenAI GPT-3 language model has been used to generate realistic-looking news articles and creative writing.
  • Translation: LLMs can be used to translate text from one language to another. This can be used for a variety of purposes, such as travel, business, and education. For example, the Google Translate app uses LLMs to translate text between over 100 languages.
  • Question answering: LLMs can be used to answer questions about a variety of topics. This can be used for a variety of purposes, such as research, education, and customer service. For example, the Google Search engine uses LLMs to provide answers to questions that users type into the search bar.
  • Code generation: LLMs can be used to generate code, such as Python scripts and Java classes. This can be used for a variety of purposes, such as software development and automation. For example, the GitHub Copilot tool uses LLMs to help developers write code more quickly and easily.
  • Data analysis: LLMs can be used to analyze large datasets of text and code. This can be used for a variety of purposes, such as fraud detection, risk assessment, and customer segmentation. For example, the Palantir Foundry platform uses LLMs to analyze data from a variety of sources to help businesses make better decisions.
  • Creative writing: LLMs can be used to generate creative text formats, such as poems, code, scripts, musical pieces, email, letters, etc. This can be used for a variety of purposes, such as entertainment, education, and marketing. For example, the Bard language model can be used to generate different creative text formats, such as poems, code, scripts, musical pieces, email, letters, etc.

 

Large Language Models Bootcamp: Learn to build your own LLM applications

Data Science Dojo’s Large Language Models Bootcamp  will teach you everything you need to know to build and deploy your own LLM applications. You’ll learn about the basics of LLMs, how to train LLMs, and how to use LLMs to build a variety of applications.

The bootcamp will be taught by experienced instructors who are experts in the field of large language models. You’ll also get hands-on experience with LLMs by building and deploying your own applications.

If you’re interested in learning more about LLMs and how to build and deploy LLM applications, then I encourage you to enroll in Data Science Dojo’s Large Language Models Bootcamp. This bootcamp is the perfect way to get started on your journey to becoming a large language model developer.

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Ayesha Saleem
| August 1, 2023

Large language models (LLMs) are a type of artificial intelligence (AI) that are trained on a massive dataset of text and code. They can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

Before we dive into the impact Large Language Models will create on different areas of work, let’s test your knowledge in the domain.

LLM quiz | Data Science Dojo

Large Language Models quiz to test your knowledge

 

 

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Why are LLMs the next big thing to learn about?

Knowing about LLMs can be important for scaling your career in a number of ways.

 

Large language model bootcamp

 

  • LLMs are becoming increasingly powerful and sophisticated. As LLMs become more powerful and sophisticated, they are being used in a variety of applications, such as machine translation, chatbots, and creative writing. This means that there is a growing demand for people who understand how to use LLMs effectively.
  • Prompt engineering is a valuable skill that can be used to improve the performance of LLMs in a variety of tasks. By understanding how to engineer prompts, you can get the most out of LLMs and use them to accomplish a variety of tasks. This is a valuable skill that can be used to improve the performance of LLMs in a variety of tasks.
  • Learning about LLMs and prompt engineering can help you to stay ahead of the curve in the field of AI. As LLMs become more powerful and sophisticated, they will have a significant impact on a variety of industries. By understanding how LLMs work, you will be better prepared to take advantage of this technology in the future.

Here are some specific examples of how knowing about LLMs can help you to scale your career:

  • If you are a software engineer, you can use LLMs to automate tasks, such as code generation and testing. This can free up your time to focus on more strategic work.
  • If you are a data scientist, you can use LLMs to analyze large datasets and extract insights. This can help you to make better decisions and improve your business performance.
  • If you are a marketer, you can use LLMs to create personalized content and generate leads. This can help you to reach your target audience and grow your business.

 

Overall, knowing about LLMs can be a valuable asset for anyone who is looking to scale their career. By understanding how LLMs work and how to use them effectively, you can become a more valuable asset to your team and your company.

Here are some additional reasons why knowing about LLMs can be important for scaling your career:

  • LLMs are becoming increasingly popular. As LLMs become more popular, there will be a growing demand for people who understand how to use them effectively. This means that there will be more opportunities for people who have knowledge of LLMs.
  • LLMs are a rapidly developing field. The field of LLMs is constantly evolving, and there are new developments happening all the time. This means that there is always something new to learn about LLMs, which can help you to stay ahead of the curve in your career.
  • LLMs are a powerful tool that can be used to solve a variety of problems. LLMs can be used to solve a variety of problems, from machine translation to creative writing. This means that there are many different ways that you can use your knowledge of LLMs to make a positive impact in the world.

 

Read more about —->> How to deploy custom LLM applications for your business 

How to build and deploy custom LLM applications for your business
Ayesha Saleem
| July 28, 2023

A custom large language model (LLM) application is a software application that is built using a custom LLM. Custom LLMs are trained on a specific dataset of text and code, which allows them to be more accurate and relevant to the specific needs of the application.

(more…)

LLM for Lawyers, enrich your precedents with the use of AI
Ayesha Saleem
| July 25, 2023

Technology has profoundly impacted the legal profession, changing how lawyers work and the services they provide to clients. In the past, lawyers spent a lot of time on tasks like manually researching case law and drafting documents. But now, LLM for lawyers can do these tasks much more quickly and efficiently.

 

Large language model bootcamp

 

For example, Electronic Document Management Systems (EDMS) allow lawyers to store and retrieve documents electronically, which saves time and reduces the risk of lost or misplaced documents. Case management software can help lawyers track deadlines, organize their case files, and communicate with clients. And online research tools make it easy for lawyers to find the latest case law and legal precedent.

These technological advancements have made it possible for lawyers to handle more cases and provide better service to their clients. But they have also changed the role of the lawyer. In the past, lawyers were primarily legal experts who provided advice and representation to clients. But now, lawyers are also technology experts who need to be able to use technology to their advantage.

This means that lawyers need to be comfortable using technology and have a basic understanding of how it works. They also need to be able to identify the right technological tools for their needs and use them effectively.

The future of the legal profession is likely to be even more technology driven. As artificial intelligence (AI) and other modern technologies become more sophisticated, they will be able to automate even more legal tasks. This will free lawyers to focus on more complex and strategic work.

But it’s important to remember that technology is just a tool. It can’t replace the human touch that is essential to the legal profession. Lawyers will always need to be able to think critically, solve problems, and communicate effectively.

 

Read more about —-> Beginner’s guide to Large Language Models

 

So, while technology is changing the legal profession, it’s not replacing lawyers. It’s simply making them more efficient and effective. And that’s a good thing for both lawyers and their clients.

 

High- tech transforming role of attorneys

LLM for lawyers
LLM for lawyers

 

Here are some specific examples of how technology has changed the role of attorneys:

  • Electronic discovery: This technology allows attorneys to search and review large amounts of electronic data, which can be a huge time-saver in complex litigation.
  • Legal research: Online legal research tools have made it much easier for attorneys to find the latest case law and legal precedent.
  • Document automation: This technology allows attorneys to create and populate legal documents with ease, which can save a lot of time and effort.
  • Online communication: Attorneys can now communicate with clients and colleagues from anywhere in the world, which can be a huge benefit for businesses with international clients.

 

Enrich precedents using LLMs

Large Language Models (LLMs) can be used to enrich precedents in a number of ways, including:

  • Identifying relevant precedents: AI can be used to search through large datasets of legal documents to identify precedents that are relevant to a particular case. This can save lawyers a lot of time and effort, as they no longer have to manually search through case law.
  • Analyzing precedents: AI can be used to analyze precedents to identify key legal concepts and arguments. This can help lawyers to better understand the precedents and to use them more effectively in their own cases.
  • Generating legal arguments: AI can be used to generate legal arguments based on precedents. This can help lawyers to quickly and easily develop strong legal arguments.
  • Predicting the outcome of cases: AI can be used to predict the outcome of cases based on precedents. This can help lawyers to make informed decisions about how to proceed with their cases.

Here are some specific examples of how LLMs can be used to enrich precedents:

  • Search through a database of case law to identify all of the cases that have been decided on a particular legal issue. This would allow a lawyer to quickly and easily see how the issue has been decided in the past, and to identify the key legal concepts and arguments that have been used in those cases.
  • Analyze a precedent to identify the key legal concepts and arguments that are used in the case. This would help a lawyer to better understand the precedent and to use it more effectively in their own cases.
  • Generate a legal argument based on a precedent. This would allow a lawyer to quickly and easily develop a strong legal argument that is supported by the precedent.
  • Predict the outcome of a case based on precedents. This would help a lawyer to make informed decisions about how to proceed with their case.

Large language model bootcamp

 

It is important to note that AI and LLMs are still under development, and they are not yet perfect. However, they have the potential to revolutionize the way that lawyers work with precedents. As AI and LLMs continue to develop, they are likely to become even more powerful tools for enriching precedents and for helping lawyers to win their cases.

 

A use case of LLM for Lawyers

Here is a real case scenario of large language models being used by a lawyer or attorney:

A lawyer is representing a client who is being sued for copyright infringement. The lawyer knows that there are a number of precedents that could be relevant to the case, but they don’t have the time to manually search through all of the case law.

The lawyer decides to use a large language model to help them identify relevant precedents. The lawyer gives the large language model a few key terms related to the case, and the large language model quickly identifies a number of precedents that are relevant to the case. The lawyer then reviews the precedents and uses them to develop a legal argument for their client.

In this case, the large language model helped the lawyer to identify relevant precedents quickly and easily. This saved the lawyer a lot of time and effort, and it allowed them to focus on developing a strong legal argument for their client.

Here are some other potential case scenarios where large language models could be used by lawyers or attorneys:

  • Preparing a contract and wants to make sure that the contract is enforceable. The lawyer could use a large language model to analyze the contract and identify any potential problems.
  • Defending a client in a criminal case and wants to find evidence that could exonerate their client. The lawyer could use a large language model to search through large datasets of data, such as social media posts and emails, to find potential evidence.
  • Representing a client in a class action lawsuit and wanting to estimate the damages that their client has suffered. The lawyer could use a large language model to analyze data, such as financial records, to estimate the damages.

 

Scale your case with AI clause assistant

AI Clause Assistant is a tool that can help you to improve your contracts by generating suggestions or improvements for existing clauses and definitions. It can also help you to write revisions without leaving your contract, and to browse through alternative versions to select the snippet that resonates most.

Here are some examples of how AI Clause Assistant can be used:

  • Drafting a contract for a new software development project. You want to make sure that the contract includes a clause that defines the scope of work. You can use AI Clause Assistant to generate a list of suggested clauses that you can use to define the scope of work.
  • Reviewing a contract that you have received from a vendor. You want to make sure that the contract includes a clause that protects your intellectual property. You can use AI Clause Assistant to generate a list of suggested clauses that you can use to protect your intellectual property.
  • Revising a contract that you have already signed. You want to make some changes to the contract, but you want to make sure that the changes are enforceable. You can use AI Clause Assistant to generate a list of suggested changes that you can make to the contract.

Here are some of the benefits of using AI Clause Assistant:

  • Save time and effort by generating suggestions for clauses and definitions.
  • Improve the quality of your contracts by providing you with suggestions that are tailored to your specific use case.
  • Avoid legal problems by providing you with suggestions that are enforceable.

Overall, AI Clause Assistant is a powerful tool that can help you to improve your contracts. It is easy to use, and it can save you time and effort. If you are looking for a way to improve your contracts, I recommend that you give AI Clause Assistant a try.

Here are some additional use cases for AI Clause Assistant:

  • Compliance: AI Clause Assistant can help you to ensure that your contracts are compliant with applicable laws and regulations.
  • Risk management: AI Clause Assistant can help you to identify and mitigate risks in your contracts.
  • Negotiation: AI Clause Assistant can help you to negotiate better contracts by providing you with insights into the strengths and weaknesses of your contracts.

 

Replace, pluralize, or singularize using LLM

Here are some steps on how to replace, pluralize, or singularize entire sections of text hassle-free:

  1. Identify the words or phrases that you want to replace or pluralize.
  2. Use a regular expression to match the words or phrases that you want to replace.
  3. Use a replacement string to replace the matched words or phrases.
  4. Use a function to pluralize or singularize the words or phrases.

Here is an example of how to replace a string of words in a contract:

This code will replace all occurrences of the word “dog” in the text with the word “dogs”.

To pluralize or singularize words, you can use the pluralize() and singularize() functions from the nltk library. For example, the following code will pluralize the word “dog”:

 

replacing, pluralizing, or singularizing entire sections of text hassle-free. This can be a useful feature for contracts, as it can help you to ensure that the text is grammatically correct and that the correct forms of words are used.

For example, let’s say you have a contract that says:

The parties agree that the contractor will be responsible for the delivery of 10 widgets.

If you want to change the number of widgets to 20, you can simply use the replace_string() function to replace the string “10” with “20”. However, this will not change the plural form of the word “widget”. To do that, you will need to use the pluralize() function. The following code will replace the string “10” with “20” and the word “widget” with the plural form “widgets”:

 

This code will print the following text:

The parties agree that the contractor will be responsible for the delivery of 20 widgets.

As you can see, the string “10” has been replaced with “20” and the word “widget” has been pluralized to “widgets”.

This is just one example of how you can use the replace_string() and pluralize() functions to replace, pluralize, or singularize entire sections of text. There are many other ways to use these functions, so you can experiment to find the best way to use them for your specific needs.

 

Adopt the best course of action with LLM

The legal landscape is becoming increasingly complex, with new legislation being passed all the time. This can make it difficult for lawyers to keep up with the latest changes, and it can also be difficult to identify the most relevant legal precedents.

Economic scenarios are also evolving quickly with new markets emerging and new technologies being developed. This can make it difficult for lawyers to advise their clients on the best course of action, and it can also be difficult to predict the potential risks and rewards of certain transactions.

Legal tech can help lawyers to address these challenges by providing them with tools that can help them to:

  • Stay up-to-date with the latest legislation. Legal tech can be used to track new legislation, to identify the most relevant legal precedents, and to stay informed of the latest legal developments.
  • Analyze complex economic scenarios. Legal tech can be used to analyze complex economic data, to identify potential risks and rewards, and to develop strategies for mitigating risk.
  • Automate repetitive tasks. Legal tech can be used to automate repetitive tasks, such as document drafting and review. This can free up lawyers’ time so that they can focus on more complex and strategic work.

As the legal landscape continues to become more complex, legal tech will play an increasingly important role in assisting lawyers. By providing lawyers with the tools, they need to stay up-to-date, analyze complex data, and automate repetitive tasks, legal tech can help lawyers to provide their clients with the best possible advice.

Furthermore, legal tech is being used to assist lawyers with the complexity of legislation and economic scenarios:

  • Document automation: Document automation tools can be used to generate contracts, wills, and other legal documents. This can save lawyers a significant amount of time and effort, and it can also help to ensure that the documents are accurate and compliant with the latest legislation.
  • E-discovery: E-discovery tools can be used to search and review large amounts of electronic data. This can be helpful in cases where there is a lot of evidence to be reviewed, or where the evidence is stored in electronic format.
  • Predictive analytics: Predictive analytics tools can be used to analyze data and identify potential risks and rewards. This can be helpful in cases where there is a lot of uncertainty, or where the potential consequences of a decision are significant

 

Upscale your legal career with Large Language Models and learn more about it in our upcoming LLM bootcamp:

 

Register today            

Master prompt engineering with effective strategies
Ayesha Saleem
| July 14, 2023

In today’s era of advanced artificial intelligence, language models like OpenAI’s GPT-3.5 have captured the world’s attention with their astonishing ability to generate human-like text. However, to harness the true potential of these models, it is crucial to master the art of prompt engineering.




How to curate a good prompt?

A well-crafted prompt holds the key to unlocking accurate, relevant, and insightful responses from language models. In this blog post, we will explore the top characteristics of a good prompt and discuss why everyone should learn prompt engineering. We will also delve into the question of whether prompt engineering might emerge as a dedicated role in the future.

Best practices for prompt engineering
Best practices for prompt engineering – Data Science Dojo

 

Prompt engineering refers to the process of designing and refining input prompts for AI language models to produce desired outputs. It involves carefully crafting the words, phrases, symbols, and formats used as input to guide the model in generating accurate and relevant responses. The goal of prompt engineering is to improve the performance and output quality of the language model.

Here’s a simple example to illustrate prompt engineering:

Imagine you are using a chatbot AI model to provide information about the weather. Instead of a generic prompt like “What’s the weather like?”, prompt engineering involves crafting a more specific and detailed prompt like “What is the current temperature in New York City?” or “Will it rain in London tomorrow?”

 

Read about —> Which AI chatbot is right for you in 2023

 

By providing a clear and specific prompt, you guide the AI model to generate a response that directly answers your question. The choice of words, context, and additional details in the prompt can influence the output of the AI model and ensure it produces accurate and relevant information.

Quick exercise –> Choose the most suitable prompt

 

Prompt engineering is crucial because it helps optimize the performance of AI models by tailoring the input prompts to the desired outcomes. It requires creativity, understanding of the language model, and attention to detail to strike the right balance between specificity and relevance in the prompts.

Different resources provide guidance on best practices and techniques for prompt engineering, considering factors like prompt formats, context, length, style, and desired output. Some platforms, such as OpenAI API, offer specific recommendations and examples for effective prompt engineering.

 

Why everyone should learn prompt engineering:

 

Prompt engineering - Marketoonist
Prompt Engineering | Credits: Marketoonist

 

1. Empowering communication: Effective communication is at the heart of every interaction. By mastering prompt engineering, individuals can enhance their ability to extract precise and informative responses from language models. Whether you are a student, professional, researcher, or simply someone seeking knowledge, prompt engineering equips you with a valuable tool to engage with AI systems more effectively.

2. Tailored and relevant information: A well-designed prompt allows you to guide the language model towards providing tailored and relevant information. By incorporating specific details and instructions, you can ensure that the generated responses align with your desired goals. Prompt engineering enables you to extract the exact information you seek, saving time and effort in sifting through irrelevant or inaccurate results.

3. Enhancing critical thinking: Crafting prompts demand careful consideration of context, clarity, and open-endedness. Engaging in prompt engineering exercises cultivates critical thinking skills by challenging individuals to think deeply about the subject matter, formulate precise questions, and explore different facets of a topic. It encourages creativity and fosters a deeper understanding of the underlying concepts.

4. Overcoming bias: Bias is a critical concern in AI systems. By learning prompt engineering, individuals can contribute to reducing bias in generated responses. Crafting neutral and unbiased prompts helps prevent the introduction of subjective or prejudiced language, resulting in more objective and balanced outcomes.

Top characteristics of a good prompt with examples

Prompting example
An example of a good prompt – Credits Gridfiti

A good prompt possesses several key characteristics that can enhance the effectiveness and quality of the responses generated. Here are the top characteristics of a good prompt:

1. Clarity:

A good prompt should be clear and concise, ensuring that the desired question or topic is easily understood. Ambiguous or vague prompts can lead to confusion and produce irrelevant or inaccurate responses.

Example:

Good Prompt: “Explain the various ways in which climate change affects the environment.”

Poor Prompt: “Climate change and the environment.”

2. Specificity:

Providing specific details or instructions in a prompt help focus the generated response. By specifying the context, parameters, or desired outcome, you can guide the language model to produce more relevant and tailored answers.

Example:

Good Prompt: “Provide three examples of how rising temperatures due to climate change impact marine ecosystems.”
Poor Prompt: “Talk about climate change.”

3. Context:

Including relevant background information or context in the prompt helps the language model understand the specific domain or subject matter. Contextual cues can improve the accuracy and depth of the generated response.

Example: 

Good Prompt: “In the context of agricultural practices, discuss how climate change affects crop yields.”

Poor Prompt: “Climate change effects

4. Open-endedness:

While specificity is important, an excessively narrow prompt may limit the creativity and breadth of the generated response. Allowing room for interpretation and open-ended exploration can lead to more interesting and diverse answers.

Example:

Good Prompt: “Describe the short-term and long-term consequences of climate change on global biodiversity.”

Poor Prompt: “List the effects of climate change.”

5. Conciseness:

Keeping the prompt concise helps ensure that the language model understands the essential elements and avoids unnecessary distractions. Lengthy or convoluted prompts might confuse the model and result in less coherent or relevant responses.

Example:
Good Prompt: “Summarize the key impacts of climate change on coastal communities.”

Poor Prompt: “Please explain the negative effects of climate change on the environment and people living near the coast.”

6. Correct grammar and syntax:

A well-structured prompt with proper grammar and syntax is easier for the language model to interpret accurately. It reduces ambiguity and improves the chances of generating coherent and well-formed responses.

Example:

Good Prompt: “Write a paragraph explaining the relationship between climate change and species extinction.”
Poor Prompt: “How species extinction climate change.”

7. Balanced complexity:

The complexity of the prompt should be appropriate for the intended task or the model’s capabilities. Extremely complex prompts may overwhelm the model, while overly simplistic prompts may not challenge it enough to produce insightful or valuable responses.

Example:

Good Prompt: “Discuss the interplay between climate change, extreme weather events, and natural disasters.”

Poor Prompt: “Climate change and weather.”

8. Diversity in phrasing:

When exploring a topic or generating multiple responses, varying the phrasing or wording of the prompt can yield diverse perspectives and insights. This prevents the model from repeating similar answers and encourages creative thinking.

Example:

Good Prompt: “How does climate change influence freshwater availability?” vs. “Explain the connection between climate change and water scarcity.”

Poor Prompt: “Climate change and water.

9. Avoiding leading or biased language:

To promote neutrality and unbiased responses, it’s important to avoid leading or biased language in the prompt. Using neutral and objective wording allows the language model to generate more impartial and balanced answers.

Example:

Good Prompt: “What are the potential environmental consequences of climate change?”

Poor Prompt: “How does climate change devastate the environment?”

10. Iterative refinement:

Crafting a good prompt often involves an iterative process. Reviewing and refining the prompt based on the generated responses can help identify areas of improvement, clarify instructions, or address any shortcomings in the initial prompt.

Example:

Prompt iteration involves an ongoing process of improvement based on previous responses and refining the prompts accordingly. Therefore, there is no specific example to provide, as it is a continuous effort.

By considering these characteristics, you can create prompts that elicit meaningful, accurate, and relevant responses from the language model.

 

Read about —-> How LLMs (Large Language Models) technology is making chatbots smarter in 2023?

 

Two different approaches of prompting

Prompting by instruction and prompting by example are two different approaches to guide AI language models in generating desired outputs. Here’s a detailed comparison of both approaches, including reasons and situations where each approach is suitable:

1. Prompting by instruction:

  • In this approach, the prompt includes explicit instructions or explicit questions that guide the AI model on how to generate the desired output.
  • It is useful when you need specific control over the generated response or when you want the model to follow a specific format or structure.
  • For example, if you want the AI model to summarize a piece of text, you can provide an explicit instruction like “Summarize the following article in three sentences.”
  • Prompting by instruction is suitable when you need a precise and specific response that adheres to a particular requirement or when you want to enforce a specific behavior in the model.
  • It provides clear guidance to the model and allows you to specify the desired outcome, length, format, style, and other specific requirements.

 

Examples of prompting by instruction:

  1. In a classroom setting, a teacher gives explicit verbal instructions to students on how to approach a new task or situation, such as explaining the steps to solve a math problem.
  2. In Applied Behavior Analysis (ABA), a therapist provides a partial physical prompt by using their hands to guide a student’s behavior in the right direction when teaching a new skill.
  3. When using AI language models, an explicit instruction prompt can be given to guide the model’s behavior. For example, providing the instruction “Summarize the following article in three sentences” to prompt the model to generate a concise summary.

 

Tips for prompting by instruction:

    • Put the instructions at the beginning of the prompt and use clear markers like “A:” to separate instructions and context.
    • Be specific, descriptive, and detailed about the desired context, outcome, format, style, etc.
    • Articulate the desired output format through examples, providing clear guidelines for the model to follow.

 

2. Prompting by example:

  • In this approach, the prompt includes examples of the desired output or similar responses that guide the AI model to generate responses based on those examples.
  • It is useful when you want the model to learn from specific examples and mimic the desired behavior.
  • For example, if you want the AI model to answer questions about a specific topic, you can provide example questions and their corresponding answers.
  • Prompting by example is suitable when you want the model to generate responses similar to the provided examples or when you want to capture the style, tone, or specific patterns from the examples.
  • It allows the model to learn from the given examples and generalize its behavior based on them.

 

Examples of prompting by example:

  1. In a classroom, a teacher shows students a model essay as an example of how to structure and write their own essays, allowing them to learn from the demonstrated example.
  2. In AI language models, providing example questions and their corresponding answers can guide the model in generating responses similar to the provided examples. This helps the model learn the desired behavior and generalize it to new questions.
  3. In an online learning environment, an instructor provides instructional prompts in response to students’ discussion forum posts, guiding the discussion and encouraging deep understanding. These prompts serve as examples for the entire class to enhance the learning experience.

 

Tips for prompting by example:

    • Provide a variety of examples to capture different aspects of the desired behavior.
    • Include both positive and negative examples to guide the model on what to do and what not to do.
    • Gradually refine the examples based on the model’s responses, iteratively improving the desired behavior.

 

Which prompting approach is right for you?

Prompting by instruction provides explicit guidance and control over the model’s behavior, while prompting by example allows the model to learn from provided examples and mimic the desired behavior. The choice between the two approaches depends on the level of control and specificity required for the task at hand. It’s also possible to combine both approaches in a single prompt to leverage the benefits of each approach for different parts of the task or desired behavior.

To become proficient in prompt engineering, register now in our upcoming Large Language Models Bootcamp

Unleash LlamaIndex: The key to uncovering deeper insights in text exploration
Muhammad Jan
| July 10, 2023

Before we understand LlamaIndex, let’s step back a bit. Imagine a futuristic landscape where machines possess an extraordinary ability to understand and produce human-like text effortlessly. LLMs have made this vision a reality. Armed with a vast ocean of training data, these marvels of innovation have become the crown jewels of the tech world.

There is no denying that LLMs (Large Language Models) are currently the talk of the town! From revolutionizing text generation and reasoning, LLMs are trained on massive datasets and have been making waves in the tech vicinity.

One particular LLM has emerged as a true superstar. Back in November 2022, ChatGPT, an LLM developed by OpenAI, attracted a staggering one million users within 5 days of its beta launch.

ChatGPT
Source: Chart: ChatGPT Sprints to One Million Users | Statista  

When researchers and developers saw these stats they started thinking on how we can best feed/augment these LLMs with our own private data. They started thinking about different solutions.

Finetune your own LLM. You adapt an existing LLM by training your data. But, this is very costly and time-consuming.

Combining all the documents into a single large prompt for an LLM might be possible now with the increased token limit of 100k for models. However, this approach could result in slower processing times and higher computational costs.

Instead of inputting all the data, selectively provide relevant information to the LLM prompt. Choose the useful bits for each query instead of including everything.

Option 3 appears to be both relevant and feasible, but it requires the development of a specialized toolkit. Recognizing this need, efforts have already begun to create the necessary tools.

Introducing LlamaIndex

Recently a toolkit was launched for building applications using LLM, known as Langchain. LlamaIndex is built on top of Langchain to provide a central interface to connect your LLMs with external data.

Key Components of LlamaIndex:

The key components of LlamaIndex are as follows

  • Data Connectors: The data connector, known as the Reader, collects data from various sources and formats, converting it into a straightforward document format with textual content and basic metadata.
  • Data Index: It is a data structure facilitating efficient retrieval of pertinent information in response to user queries. At a broad level, Indices are constructed using Documents and serve as the foundation for Query Engines and Chat Engines, enabling seamless interactions and question-and-answer capabilities based on the underlying data. Internally, Indices store data within Node objects, which represent segments of the original documents.
  • Retrievers: Retrievers play a crucial role in obtaining the most pertinent information based on user queries or chat messages. They can be constructed based on Indices or as standalone components and serve as a fundamental element in Query Engines and Chat Engines for retrieving contextually relevant data.
  • Query Engines: A query engine is a versatile interface that enables users to pose questions regarding their data. By accepting natural language queries, the query engine provides comprehensive and informative responses.
  • Chat Engines: A chat engine serves as an advanced interface for engaging in interactive conversations with your data, allowing for multiple exchanges instead of a single question-and-answer format. Similar to ChatGPT but enhanced with access to a knowledge base, the chat engine maintains a contextual understanding by retaining the conversation history and can provide answers that consider the relevant past context.

Difference between query engine and chat engine:

It is important to note that there is a significant distinction between a query engine and a chat engine. Although they may appear similar at first glance, they serve different purposes:

A query engine operates as an independent system that handles individual questions over the data without maintaining a record of the conversation history.

On the other hand, a chat engine is designed to keep track of the entire conversation history, allowing users to query both the data and previous responses. This functionality resembles ChatGPT, where the chat engine leverages the context of past exchanges to provide more comprehensive and contextually relevant answers

  • Customization: LlamaIndex offers customization options where you can modify the default settings, such as the utilization of OpenAI’s text-davinci-003 model. Users have the flexibility to customize the underlying language model (LLM) and other settings used in LlamaIndex, with support for various integrations and LangChain’s LLM modules.
  • Analysis: LlamaIndex offers a diverse range of analysis tools for examining indices and queries. These tools include features for analyzing token usage and associated costs. Additionally, LlamaIndex provides a Playground module, which presents a visual interface for analyzing token usage across different index structures and evaluating performance metrics.
  • Structured Outputs: LlamaIndex offers an assortment of modules that empower language models (LLMs) to generate structured outputs. These modules are available at various levels of abstraction, providing flexibility and versatility in producing organized and formatted results.
  • Evaluation: LlamaIndex provides essential modules for assessing the quality of both document retrieval and response synthesis. These modules enable the evaluation of “hallucination,” which refers to situations where the generated response does not align with the retrieved sources. A hallucination occurs when the model generates an answer without effectively grounding it in the given contextual information from the prompt.
  • Integrations: LlamaIndex offers a wide array of integrations with various toolsets and storage providers. These integrations encompass features such as utilizing vector stores, integrating with ChatGPT plugins, compatibility with Langchain, and the capability to trace with Graphsignal. These integrations enhance the functionality and versatility of LlamaIndex by allowing seamless interaction with different tools and platforms.
  • Callbacks: LlamaIndex offers a callback feature that assists in debugging, tracking, and tracing the internal operations of the library. The callback manager allows for the addition of multiple callbacks as required. These callbacks not only log event-related data but also track the duration and frequency of each event occurrence. Moreover, a trace map of events is recorded, providing valuable information that callbacks can utilize in a manner that best suits their specific needs.
  • Storage: LlamaIndex offers a user-friendly interface that simplifies the process of ingesting, indexing, and querying external data. By abstracting away complexities, LlamaIndex allows users to query their data with just a few lines of code. Behind the scenes, LlamaIndex provides the flexibility to customize storage components for different purposes. This includes document stores for storing ingested documents (represented as Node objects), index stores for storing index metadata, and vector stores for storing embedding vectors.The document and index stores utilize a shared key-value store abstraction, providing a common framework for efficient storage and retrieval of data

Now that we have explored the key components of LlamaIndex, let’s delve into its operational mechanisms and understand how it functions.

How Llama-Index Works:

To begin, the first step is to import the documents into LlamaIndex, which provides various pre-existing readers for sources like databases, Discord, Slack, Google Sheets, Notion, and the one we will utilize today, the Simple Directory Reader, among others.[Text Wrapping Break][Text Wrapping Break]You can check for more here: Llama Hub (llama-hub-ui.vercel.app)

Once the documents are loaded, LlamaIndex proceeds to parse them into nodes, which are essentially segments of text. Subsequently, an index is constructed to enable quick retrieval of relevant data when querying the documents. The index can be stored in different formats, but we will opt for a Vector Store as it is typically the most useful when querying text documents without specific limitations.

LlamaIndex is built upon LangChain, which serves as the foundational framework for a wide range of LLM applications. While LangChain provides the fundamental building blocks, LlamaIndex is specifically designed to streamline the workflow described above.

Here is an example code showcasing the utilization of the SimpleDirectoryReader data loader in LlamaIndex, along with the integration of the OpenAI language model for natural language processing.

Installing the necessary libraries required to run the code.


Importing openai library and setting the secret API (Application Programming Interface) key.


Importing the SimpleDirectoryReader class from llama_index library and loading the data from it.


Importing SimpleNodeParser class from llama_index and parsing the documents into nodes – basically in chunks of text.


Importing VectorStoreIndex class from llama_index to create index from the chunks of text so that each time when a query is placed only relevant data is sent to OpenAI. In short, for the sake of cost effectiveness.

Conclusion:

LlamaIndex, built on top of Langchain, offers a powerful toolkit for integrating external data with LLMs. By parsing documents into nodes, constructing an efficient index, and selectively querying relevant information, LlamaIndex enables cost-effective exploration of text data.

The provided code example demonstrates the utilization of LlamaIndex’s data loader and query engine, showcasing its potential for next-generation text exploration. For the notebook of the above code, refer to the source code available here.

Transforming finance: The power of Large Language Models in the financial industry
Ayesha Saleem
| July 3, 2023

Over the past few years, a shift has shifted from Natural Language Processing (NLP) to the emergence of Large Language Models (LLMs). This evolution is fueled by the exponential expansion of available data and the successful implementation of the Transformer architecture. Transformers, a type of Deep Learning model, have played a crucial role in the rise of LLMs.

Significance of Large Language Models

LLMs are a transformative technology that has revolutionized the way businesses operate. Their significance lies in their ability to understand, interpret, and generate human language based on vast amounts of data. These models can recognize, summarize, translate, predict, and generate text and other forms of content with exceptional accuracy. LLMs broaden AI’s reach across industries, enabling new research, creativity, and productivity waves.

 

LLMs in finance
LLMs in finance – Source Semantic Scholars

Applications of LLMs in the finance industry

Applications of Large Language Models (LLMs) in the finance industry have gained significant traction in recent years. LLMs, such as GPT-4, BERT, RoBERTa, and specialized models like BloombergGPT, have demonstrated their potential to revolutionize various aspects of the fintech sector. These cutting-edge technologies offer several benefits and opportunities for both businesses and individuals within the finance industry.

1. Fraud detection and prevention:

LLMs powered by AI can analyze large volumes of financial data in real time, enabling more effective detection of fraudulent activities. By examining patterns and identifying unusual behaviors, LLMs can enhance fraud detection capabilities and reduce financial losses for businesses and individuals.

2. Risk assessment and management:

Financial institutions can leverage LLMs to evaluate risk levels associated with customers, loans, and investments with greater accuracy. By analyzing diverse data sources and incorporating advanced machine learning algorithms, LLMs enable more informed decision-making, minimizing potential risks.

3. Personalized customer service:

AI-driven chatbots and virtual assistants, powered by LLMs, can provide highly customized customer experiences in the finance industry. These conversational agents can handle a broad range of customer inquiries, offering tailored financial advice and resolving queries around the clock. By enhancing customer service capabilities, LLMs contribute to improved customer satisfaction and increased operational efficiency for financial institutions.

4. Efficient onboarding:

LLMs can assist in the onboarding process for new customers by guiding them through account setup, answering their questions, and providing personalized recommendations for financial products and services. This streamlined onboarding experience improves customer satisfaction and helps financial institutions acquire and retain customers more effectively.

5. Advanced financial advice:

LLMs enable financial advisors to offer customized financial guidance to their clients. By leveraging the capabilities of LLMs, advisors can provide personalized recommendations for investments, retirement planning, and other financial decisions. These AI-powered models assist clients in making well-informed decisions and enhance the overall quality of financial advice.

6. News analysis and sentiment detection:

LLMs, like BloombergGPT, are specifically designed for the finance industry. They can analyze news headlines, earnings reports, social media feeds, and other sources of information to identify relevant trends and patterns. These models can also detect sentiment in news articles, helping traders and investors make informed decisions based on market sentiment.

7. Data analysis and predictive analytics:

LLMs can analyze large amounts of financial data, identify patterns, and make accurate predictions. This capability is particularly valuable for tasks such as market forecasting, investment analysis, and portfolio optimization. By harnessing the power of LLMs, financial institutions can gain valuable insights and make data-driven decisions

How large language models can automate financial services

Large language models have the potential to automate various financial services, including customer support and financial planning. These models, such as GPT (Generative Pre-trained Transformer), have been developed specifically for the financial services industry to accelerate digital transformation and improve competitiveness.

 

Read about —> How LLMs (Large Language Models) technology is making chatbots smarter in 2023?

 

One example of a large language model designed for banking is SambaNova GPT Banking. This solution aims to address the deep learning deployment gap in the banking sector by jump-starting banks’ deep learning language capabilities in a matter of weeks, rather than years [1]. By subscribing to GPT Banking, banks can leverage the technology to perform various tasks:

1. Sentiment analysis:

GPT Banking can scan social media, press, and blogs to understand market, investor, and stakeholder sentiment.

2. Entity recognition:

It reduces human error by classifying documents and minimizing manual and repetitive work.

3. Language generation:

The model can process, transcribe, and prioritize claims, extract necessary information, and create documents to enhance customer satisfaction.

4. Language translation:

GPT Banking enables language translation to expand the customer base.

The deployment of large language models like GPT Banking offers several benefits to financial institutions:

5. Efficiency and time-saving:

By automating routine tasks, these models can enhance efficiency and productivity for financial service providers. AI-powered assistants can handle activities such as scheduling appointments, answering frequently asked questions, and providing essential financial advice, allowing human professionals to focus on more strategic and value-added tasks.

6. Personalized customer experience:

Large language models can provide instant and personalized responses to customer queries, enabling financial advisors to deliver real-time information and tailor advice to individual clients. This enhances the overall client experience and satisfaction.

7. Competitive advantage:

Embracing AI technologies like large language models can give financial institutions a competitive edge. Early adopters can differentiate themselves by leveraging the power of AI to enhance their client experience, improve efficiency, and stay ahead of their competitors in the rapidly evolving financial industry.

Upscale finance sector with LLMs

It’s worth noting that large language models can handle natural language processing tasks in diverse domains, and LLMs in the finance sector, they can be used for applications like robo-advising, algorithmic trading, and low-code development. These models leverage vast amounts of training data to simulate human-like understanding and generate relevant responses, enabling sophisticated interactions between financial advisors and clients.

Overall, large language models have the potential to significantly streamline financial services by automating tasks, improving efficiency, enhancing customer experience, and providing a competitive edge to financial institutions.

If you want to excel in Large Language Models, register today in our upcoming LLM Bootcamp.

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Roger Brown
| June 26, 2023

A large language model is a computer program that is trained and learns from a large amount of data. The machine is capable of understanding and generating human-like text based on the patterns and knowledge accumulated during the training process.

In the library, for example, a young person or child may read various books, articles, and writings from a wide variety of authors. Reading and comprehending all that information requires a great deal of time. In time, you will become familiar with a wide range of topics, and you will be able to answer questions about them and discuss them in meaningful and logical ways.

Large language models follow similar principles. The program reads and analyzes a vast amount of text, including books, websites, and articles. Therefore, it is able to learn the meaning of words, the structure of words, and the relation between them. In response to the input it receives, the model will be capable of providing explanations, generating responses, or initiating conversations based on the information it receives after training. On the basis of the text that is provided, the system is able to generate coherent and relevant responses by using context.

Large language models and chatbots
Large language models and chatbots

The purpose of a large language model is to create a computer program that can generate human-like text based on the knowledge it has acquired through reading.

 

Artificial intelligence systems that are capable of understanding and generating human language are known as large Language Models (LLMs). In order to learn the nuances of language and to respond coherently and pertinently, deep learning algorithms are used along with a large amount of data. An LLM is generally able to predict what words will follow words already typed.

By typing a few keywords into the search box, Google’s BERT system can predict what you will be searching for. The BERT algorithm has been trained on 3.3 million words and contains 340 million parameters so that it can understand and respond to what is entered into the search box.

 

Large language model bootcamp

 

One of the most widely known LLMs today is ChatGPT, which was developed by OpenAI. The service has been registered by more than one million users since it was first made available to the public. A little over two months after the company’s launch, Instagram reached a million downloads, whereas Spotify took five months to reach that level.

It is no wonder that ChatGPT has experienced explosive growth due to its ability to mimic human responses as closely as possible. A total of 300 million words and 175 billion parameters have been analyzed by BERT’s machine learning algorithms, which far exceed the training model used by the model.

Most popular LLMs (Large Language Models)

It is currently commonplace for multiple companies to develop large language models that have been trained on billions of variables and datasets. However, we are going to take a look at some of the top LLM programs right now:

  • A large language model that was released in 2020, Generative Pre-trained Transformer 3 (GPT-3), has grown in popularity over the years. As part of its development, OpenAI developed the GPT-3 code which has now been licensed to Microsoft for modification and usage.

A prompt is given to GPT-3 and it produces very accurate human-like text output based on deep learning. AI chatbot ChatGPT is based on GPT-3.5, one of the most popular AI chatbots. As well as offering a public API, ChatGPT provides an API through which the results of chats may be integrated and received.

 

  • A Google AI language model called Bidirectional Encoder Representations from Transformers (BERT) was introduced in 2018. A notable feature of this NLP model is that it finds relevance in both sides (left/right) of a word at the same time. Pre-trained plain text data sources, such as Wikipedia, are used by BERT to understand a prompt in a deeper and more meaningful way.

 

  • In 2022, Google developed a conversational large language model in the form of Language Model for Dialogue Applications (LaMDA). As part of the training process, it utilizes a decoding-only transformer language model as well as a text corpus consisting of 1.56 trillion words that have been pre-trained on both documents and dialogues. In addition to providing a Generic Language API to integrate with third-party applications, LaMDA powers Google’s conversational AI chatbot – Bard.

 

  • By 2022, Google AI had developed a large language model based on artificial intelligence called Pathways Language Model (PALM). This system is trained by using a variety of high-quality datasets, which include filtered web pages, books, Wikipedia articles, news articles, source code taken from GitHub repositories, and social media communications.

 

  • A large language model meta-AI (LLaMA) is expected to be developed in Facebook by 2023. It is similar to other large language models that LLaMA models generate text indefinitely based on a sequence of words. By using texts from 20 of the world’s most popular languages, the developers trained the LLaMA model using Latin and Cyrillic alphabets.

 

  • OpenAI created the Generated Pretrained Transformer 4 (GPT-4) model to model multimodal large languages. In addition to taking images and text as inputs, it is an improved version of GPT-3. A number of APIs can be used, images can be generated, and webpages can be accessed and summarized using GPT-4. In addition, ChatGPT Plus is powered by it.

 

Key points to ponder about how LLMs have influenced the e-commerce industry:

  • Show customers what they want: LMs can analyze customer data, such as browsing history, purchase patterns, and preferences, to make highly personalized product recommendations. They can improve customer satisfaction by understanding customers’ needs and preferences.
  • Dedicated Shopping Assistant: It can act as a virtual shopping assistant, assisting customers with navigation through product catalogs, answering questions, and providing guidance. Language Models provide customers with an interactive and personalized shopping experience by allowing them to communicate in natural language.
  • Search & Discover like Humans: They are capable of understanding complex search queries and providing accurate and relevant search results. A better search experience on e-commerce platforms is enabled as a result of this. Customers are able to find products more quickly and easily.
  • Save Time with negligible human intervention: Chatbots are used to provide customer service based on LMs. In addition to handling order tracking, returns, and general product inquiries, customer service representatives can also handle several types of inquiries from customers. By implementing Language Models that can provide real-time responses, customer service can be improved, and human intervention can be reduced.
  • Read, Learn, and then Decide: A LM is capable of producing natural language product descriptions that are engaging to the reader. Customers are also able to gain an understanding of the product’s features, benefits, and applications as well as make informed decisions.
  • Customer Emotions Matter: Customer reviews and feedback can be analyzed by LMs in order to gain insight and better understand customer sentiment. E-commerce platforms are able to identify trends, improve product quality, and address customer concerns in a timely manner through this process.
  • Zero Language Barrier: LMs are capable of assisting in the translation of foreign languages, breaking down language barriers for international customers. Thus, empowering e-commerce platforms to widen their prospects and reach a global audience and thereby, expand their customer base.
  • Voice of the Customer: LMs facilitate voice-based shopping experiences thanks to advancements in speech recognition technology. In order to provide customers with a convenient and hands-free shopping experience, voice commands are available for searching for products, adding items to their shopping carts, and completing purchases.
  • Learn from the Present, Prepare for the Future: In order to obtain insight into customer sentiment, LMs analyze customer reviews and feedback and analyze customer feedback. A company’s e-commerce platform can use this process to identify trends, improve product quality, and respond to customer complaints in a timely manner as a result of their efforts.

Conventional chatbots are typically developed through the use of specific frameworks or programming languages.

The definition of explicit rules and the updating of those rules periodically are essential in order to deal with new scenarios. It requires significant computational resources and expertise to develop, train, and maintain LLM-based chatbots.

 

Aspect

LLM-based Chatbots

Traditional Chatbots

Technology

Based on advanced deep learning

Rule-based or scripted approaches

architectures (e.g., GPT)

Language Understanding

A better understanding of natural

Limited ability for complex

language and context

language understanding

Conversational Ability

More human-like and coherent conversations Prone to scripted responses and struggles with complex dialogs

Personalization

Offers more personalized experiences

Lacks advanced personalization
Training and Adaptability Requires extensive pre-training

Requires manual rule updates for

and fine-tuning on specific tasks

new scenarios

Limitations

Can generate incorrect or misleading Less prone to generating
responses, lacks common sense

incorrect or unexpected responses

Development and Maintenance

Requires significant computational

Developed using specific

 

Developing LLM-based Chatbots requires high-quality Annotated Data

A large language model (LLM) is a powerful tool that enables you to enhance your ability to understand natural language and generate text that appears human-like. As a result of these sophisticated models, chatbots in various fields, including the e-commerce industry, could be revolutionized in terms of how they interact with users. A chatbot that is based on LLM will likely be more effective if the training data it receives is of high quality.

Annotating data is an essential component of preparing training data for LLMs. A dataset is labelled or tagged with annotations in order for machine learning algorithms to understand it. LLM-based chatbots are developed by annotating text with data such as intent, entities, sentiment, and dialogue structure. Based on this annotated data, the bot can provide users with relevant answers to their queries and engage in meaningful dialogue with them.

 

 

In order to train LLM-based chatbots, the quality of annotated data is of paramount importance. Annotations of high quality help the chatbot understand users’ queries accurately, understand the nuances of their language, and respond appropriately to them. It is possible that chatbots will be unable to interpret complex language structures, comprehend the intent of the user, or generate coherent and contextually relevant responses without well-annotated data.

The process of data annotation requires annotators who are skilled at interpreting and labeling data accurately as well as having a deep understanding of language. The annotators are capable of capturing subtle nuances, idioms, and context by utilizing their expertise in linguistics and domain knowledge. Their meticulous labeling and annotation of the data during the training process provide the LLM with the guidance it needs to learn from the examples and generalize from them.

LLM-based chatbots benefit from highly annotated data in numerous ways:

Understanding language: As a result of annotations, users are able to gain an understanding of the meaning, intent, and entities represented in their queries. As a result, the chatbot is capable of understanding nuances in the language of a user, interpreting their intent accurately, and providing relevant information based on their input.

Understanding context: A chatbot can understand the conversation flow based on annotations, which provide context cues. The chatbot develops a greater understanding of a conversation by annotating dialogue structure and conversation context, thereby ensuring more coherent and contextually relevant responses.

Enhanced response generation:

When annotations are of high quality, they contribute to the production of more accurate and contextually appropriate responses. LLM-based chatbots are trained on well-annotated data in order to generate text that is human-like and aligns with the conversation’s intention and context.

Expertise in a specific domain:

It is also possible to tailor data annotations for specific e-commerce domains. In order to be able to provide users with more accurate and informed responses, the chatbot acquires domain knowledge from product descriptions, customer reviews, and other domain-specific sources.

As a result, it cannot be overstated just how important it is to use high-quality annotated data to train LLM-based chatbots. It provides the basis for the development of these chatbots’ abilities to understand and respond to natural language. An e-commerce business should partner with a data annotation company that specializes in LLM training in order to ensure the accuracy, performance, and effectiveness of their chatbot solutions. An LLM-based chatbot can provide outstanding customer service, personalized suggestions, and seamless interaction as a result of quality annotations.

Final thoughts

The article describes how large language models (LLMs) affect the e-commerce industry. A LLM, such as GPT-3 or BERT, is an advanced deep-learning model capable of interpreting and generating human-like text after extensive training on large datasets. By understanding natural language, engaging in conversations, personalizing, and performing improved search functions, they have revolutionized chatbot technology.

Data that has been labeled with annotations such as intent, entities, sentiment, and dialogue structure is required for the training of LLM-based chatbots. With well-annotated data, chatbots can provide contextually relevant responses to users based on their questions, take into account nuances in language, and understand nuances in user queries. The article emphasizes the importance of partnering with companies that specialize in LLM training to ensure the effectiveness and accuracy of chatbot solutions in e-commerce.

Data Science Dojo’s Large Language Models Bootcamp

Introducing Data Science Dojo’s Large Language Models Bootcamp, a specialized 40-hour program for creating LLM-powered applications. This intensive course concentrates on practical aspects of LLMs in natural language processing, utilizing libraries like Hugging Face and LangChain.

Participants will master text analytics techniques, including semantic search and Generative AI. Perfect for professionals seeking to enhance their understanding of Generative AI, the program covers essential principles and real-world implementation without the need for extensive coding skills.

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Unleashing the power of LangChain: A comprehensive guide to building custom Q&A chatbots 
Syed Hyder Ali Zaidi
| May 22, 2023

The NLP landscape has been revolutionized by the advent of large language models (LLMs) like GPT-3 and GPT-4. These models have laid a strong foundation for creating powerful, scalable applications. However, the potential of these models is greatly influenced by the quality of the prompt, highlighting the importance of prompt engineering.

Furthermore, real-world NLP applications often require more complexity than a single ChatGPT session can provide. This is where LangChain comes into play! 

 

Get more information on Large Language models and its applications and tools by clicking below:


Large language model bootcamp

 

Harrison Chase’s brainchild, LangChain, is a Python library designed to help you leverage the power of LLMs to build custom NLP applications. As of May 2023, this game-changing library has already garnered almost 40,000 stars on GitHub. 

LangChain

 

Interested in learning about Large Language Models and building custom ChatGPT like applications for your business? Click below

Learn More                  

 

This comprehensive beginner’s guide provides a thorough introduction to LangChain, offering a detailed exploration of its core features. It walks you through the process of building a basic application using LangChain and shares valuable tips and industry best practices to make the most of this powerful framework. Whether you’re new to Language Learning Models (LLMs) or looking for a more efficient way to develop language generation applications, this guide serves as a valuable resource to help you leverage the capabilities of LLMs with LangChain. 

Overview of LangChain Modules 

These modules serve as fundamental abstractions that form the foundation of any application powered by the Language Model (LLM). LangChain offers standardized and adaptable interfaces for each module. Additionally, LangChain provides external integrations and even ready-made implementations for seamless usage. Let’s delve deeper into these modules. 

Overview of LangChain Modules
Overview of LangChain Modules

LLM: 

LLM is the fundamental component of LangChain. It is essentially a wrapper around a large language model that helps use the functionality and capability of a specific large language model. 

Chains:

As stated earlier, LLM (Language Model) serves as the fundamental unit within LangChain. However, in line with the “LangChain” concept, it offers the ability to link together multiple LLM calls to address specific objectives. 

For instance, you may have a need to retrieve data from a specific URL, summarize the retrieved text, and utilize the resulting summary to answer questions. 

On the other hand, chains can also be simpler in nature. For instance, you might want to gather user input, construct a prompt using that input, and generate a response based on the constructed prompt. 

Prompts: 

Prompts have become a popular modeling approach in programming. It simplifies prompt creation and management with specialized classes and functions, including the essential PromptTemplate. 

Document Loaders and Utils: 

LangChain’s Document Loaders and Utils modules simplify data access and computation. Document loaders convert diverse data sources into text for processing, while the utils module offers interactive system sessions and code snippets for mathematical computations. 

Vectorstores: 

The widely used index type involves generating numerical embeddings for each document using an Embedding Model. These embeddings, along with the associated documents, are stored in a vectorstore. This vectorstore enables efficient retrieval of relevant documents based on their embeddings. 

Agents: 

LangChain offers a flexible approach for tasks where the sequence of language model calls is not deterministic. Its “Agents” can act based on user input and previous responses. The library also integrates with vector databases and has memory capabilities to retain the state between calls, enabling more advanced interactions. 

Building Our App 

Now that we’ve gained an understanding of LangChain, let’s build a PDF Q/A Bot app using LangChain and OpenAI. Let me first show you the architecture diagram for our app and then we will start with our app creation. 

QA Chatbot Architecture
QA Chatbot Architecture

 

Below is an example code that demonstrates the architecture of a PDF Q&A chatbot powered by the new technology. This code utilizes the OpenAI language model for natural language processing, FAISS database for efficient similarity search, PyPDF2 for reading PDF files, and Streamlit for creating a web application interface. The chatbot leverages LangChain’s Conversational Retrieval Chain to find the most relevant answer from a document based on the user’s question. This integrated setup enables an interactive and accurate question-answering experience for the users. 

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Importing necessary libraries 

Import Statements: These lines import the necessary libraries and functions required to run the application. 

  • PyPDF2: Python library used to read and manipulate PDF files. 
  • langchain: a framework for developing applications powered by language models. 
  • streamlit: A Python library used to create web applications quickly. 
Importing necessary libraries
Importing necessary libraries

If the LangChain and OpenAI are not installed already, you first need to run the following commands in the terminal. 

Install LangChain

Setting OpenAI API Key 

You will replace the placeholder with your OpenAI API key which you can access from OpenAI API. The above line sets the OpenAI API key, which you need to use OpenAI’s language models. 

Setting OpenAI API Key

Streamlit UI 

These lines of code create the web interface using Streamlit. The user is prompted to upload a PDF file.

Streamlit UI
Streamlit UI

Reading the PDF File 

If a file has been uploaded, this block reads the PDF file, extracts the text from each page, and concatenates it into a single string. 

Reading the PDF File
Reading the PDF File

Text Splitting 

Language Models are often limited by the amount of text that you can pass to them. Therefore, it is necessary to split them up into smaller chunks. It provides several utilities for doing so. 

Text Splitting 
Text Splitting

Using a Text Splitter can also help improve the results from vector store searches, as eg. smaller chunks may sometimes be more likely to match a query. Here we are splitting the text into 1k tokens with 200 tokens overlap. 

Embeddings 

Here, the OpenAIEmbeddings function is used to download embeddings, which are vector representations of the text data. These embeddings are then used with FAISS to create an efficient search index from the chunks of text.  

Embeddings
Embeddings

Creating Conversational Retrieval Chain 

The chains developed are modular components that can be easily reused and connected. They consist of predefined sequences of actions encapsulated in a single line of code. With these chains, there’s no need to explicitly call the GPT model or define prompt properties. This specific chain allows you to engage in conversation while referencing documents and retains a history of interactions. 

Creating Conversational Retrieval Chain
Creating Conversational Retrieval Chain

Streamlit for Generating Responses and Displaying in the App 

This block prepares a response that includes the generated answer and the source documents and displays it on the web interface. 

Streamlit for Generating Responses and Displaying in the App
Streamlit for Generating Responses and Displaying in the App

Let’s Run Our App 

QA Chatbot
QA Chatbot

Here we uploaded a PDF, asked a question, and got our required answer with the source document. See, that is how the magic of LangChain works.  

You can find the code for this app on my GitHub repository LangChain-Custom-PDF-Chatbot.

Wrapping Up 

Concluding the journey! Mastering LangChain for creating a basic Q&A application has been a success. I trust you have acquired a fundamental comprehension of LangChain’s potential. Now, take the initiative to delve into LangChain further and construct even more captivating applications. Enjoy the coding adventure.

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