llms

Welcome to the world of open-source (LLMs) large language models, where the future of technology meets community spirit. By breaking down the barriers of proprietary systems, open language models invite developers, researchers, and enthusiasts from around the globe to contribute to, modify, and improve upon the foundational models.

This collaborative spirit not only accelerates advancements in the field but also ensures that the benefits of AI technology are accessible to a broader audience. As we navigate through the intricacies of open-source language models, we’ll uncover the challenges and opportunities that come with adopting an open-source model, the ecosystems that support these endeavors, and the real-world applications that are transforming industries.

Benefits of open-source LLMs

As soon as ChatGPT was revealed, OpenAI’s GPT models quickly rose to prominence. However, businesses began to recognize the high costs associated with closed-source models, questioning the value of investing in large models that lacked specific knowledge about their operations.

In response, many opted for smaller open LLMs, utilizing Retriever-And-Generator (RAG) pipelines to integrate their data, achieving comparable or even superior efficiency.

There are several advantages to closed-source large language models worth considering.

1. Cost-effectiveness:

Open-source Large Language Models (LLMs) present a cost-effective alternative to their proprietary counterparts, offering organizations a financially viable means to harness AI capabilities.

• No licensing fees are required, significantly lowering initial and ongoing expenses.
• Organizations can freely deploy these models, leading to direct cost reductions.
• Open large language models allow for specific customization, enhancing efficiency without the need for vendor-specific customization services.

2. Flexibility:

Companies are increasingly preferring the flexibility to switch between open and proprietary (closed) models to mitigate risks associated with relying solely on one type of model.

This flexibility is crucial because a model provider’s unexpected update or failure to keep the model current can negatively affect a company’s operations and customer experience.

Companies often lean towards open language models when they want more control over their data and the ability to fine-tune models for specific tasks using their data, making the model more effective for their unique needs.

3. Data ownership and control:

Companies leveraging open-source language models gain significant control and ownership over their data, enhancing security and compliance through various mechanisms. Here’s a concise overview of the benefits and controls offered by using open large language models:

Data hosting control:

• Choice of data hosting on-premises or with trusted cloud providers.
• Crucial for protecting sensitive data and ensuring regulatory compliance.

Internal data processing:

• Avoids sending sensitive data to external servers.
• Reduces the risk of data breaches and enhances privacy.

Customizable data security features:

• Flexibility to implement data anonymization and encryption.
• Helps comply with data protection laws like GDPR and CCPA.

Transparency and audibility:

• The open-source nature allows for code and process audits.
• Ensures alignment with internal and external compliance standards.

Examples of enterprises leveraging open-source LLMs

Here are examples of how different companies around the globe have started leveraging open language models.

1. VMWare

VMWare, a noted enterprise in the field of cloud computing and digitalization, has deployed an open language model called the HuggingFace StarCoder. Their motivation for using this model is to enhance the productivity of their developers by assisting them in generating code.

This strategic move suggests VMware’s priority for internal code security and the desire to host the model on their infrastructure. It contrasts with using an external system like Microsoft-owned GitHub’s Copilot, possibly due to sensitivities around their codebase and not wanting to give Microsoft access to it

2. Brave

Brave, the security-focused web browser company, has deployed an open-source large language model called Mixtral 8x7B from Mistral AI for their conversational assistant named Leo, which aims to differentiate the company by emphasizing privacy.

Previously, Leo utilized the Llama 2 model, but Brave has since updated the assistant to default to the Mixtral 8x7B model. This move illustrates the company’s commitment to integrating open LLM technologies to maintain user privacy and enhance their browser’s functionality.

3. Gab Wireless

Gab Wireless, the company focused on child-friendly mobile phone services, is using a suite of open-source models from Hugging Face to add a security layer to its messaging system. The aim is to screen the messages sent and received by children to ensure that no inappropriate content is involved in their communications. This usage of open language models helps Gab Wireless ensure safety and security in children’s interactions, particularly with individuals they do not know.

4. IBM

IBM actively incorporates open models across various operational areas.

• AskHR application: Utilizes IBM’s Watson Orchestration and open language models for efficient HR query resolution.
• Consulting advantage tool: Features a “Library of Assistants” powered by IBM’s wasonx platform and open-source large language models, aiding consultants.
• Marketing initiatives: Employs an LLM-driven application, integrated with Adobe Firefly, for innovative content and image generation in marketing.

5. Intuit

Intuit, the company behind TurboTax, QuickBooks, and Mailchimp, has developed its language models incorporating open LLMs into the mix. These models are key components of Intuit Assist, a feature designed to help users with customer support, analysis, and completing various tasks. The company’s approach to building these large language models involves using open-source frameworks, augmented with Intuit’s unique, proprietary data.

6. Shopify

Shopify has employed publically available language models in the form of Shopify Sidekick, an AI-powered tool that utilizes Llama 2. This tool assists small business owners with automating tasks related to managing their commerce websites. It can generate product descriptions, respond to customer inquiries, and create marketing content, thereby helping merchants save time and streamline their operations.

7. LyRise

LyRise, a U.S.-based talent-matching startup, utilizes open language models by employing a chatbot built on Llama, which operates similarly to a human recruiter. This chatbot assists businesses in finding and hiring top AI and data talent, drawing from a pool of high-quality profiles in Africa across various industries.

8. Niantic

Niantic, known for creating Pokémon Go, has integrated open-source large language models into its game through the new feature called Peridot. This feature uses Llama 2 to generate environment-specific reactions and animations for the pet characters, enhancing the gaming experience by making character interactions more dynamic and context-aware.

9. Perplexity

Here’s how Perplexity leverages open-source LLMs

• Response generation process:

When a user poses a question, Perplexity’s engine executes approximately six steps to craft a response. This process involves the use of multiple language models, showcasing the company’s commitment to delivering comprehensive and accurate answers.

In a crucial phase of response preparation, specifically the second-to-last step, Perplexity employs its own specially developed open-source language models. These models, which are enhancements of existing frameworks like Mistral and Llama, are tailored to succinctly summarize content relevant to the user’s inquiry.

The fine-tuning of these models is conducted on AWS Bedrock, emphasizing the choice of open models for greater customization and control. This strategy underlines Perplexity’s dedication to refining its technology to produce superior outcomes.

• Partnership and API integration:

Expanding its technological reach, Perplexity has entered into a partnership with Rabbit to incorporate its open-source large language models into the R1, a compact AI device. This collaboration facilitated through an API, extends the application of Perplexity’s innovative models, marking a significant stride in practical AI deployment.

10. CyberAgent

CyberAgent, a Japanese digital advertising firm, leverages open language models with its OpenCALM initiative, a customizable Japanese language model enhancing its AI-driven advertising services like Kiwami Prediction AI. By adopting an open-source approach, CyberAgent aims to encourage collaborative AI development and gain external insights, fostering AI advancements in Japan. Furthermore, a partnership with Dell Technologies has upgraded their server and GPU capabilities, significantly boosting model performance (up to 5.14 times faster), thereby streamlining service updates and enhancements for greater efficiency and cost-effectiveness.

Challenges of open-source LLMs

While open LLMs offer numerous benefits, there are substantial challenges that can plague the users.

1. Customization necessity:

Open language models often come as general-purpose models, necessitating significant customization to align with an enterprise’s unique workflows and operational processes. This customization is crucial for the models to deliver value, requiring enterprises to invest in development resources to adapt these models to their specific needs.

2. Support and governance:

Unlike proprietary models that offer dedicated support and clear governance structures, publically available large language models present challenges in managing support and ensuring proper governance. Enterprises must navigate these challenges by either developing internal expertise or engaging with the open-source community for support, which can vary in responsiveness and expertise.

3. Reliability of techniques:

Techniques like Retrieval-Augmented Generation aim to enhance language models by incorporating proprietary data. However, these techniques are not foolproof and can sometimes introduce inaccuracies or inconsistencies, posing challenges in ensuring the reliability of the model outputs.

4. Language support:

While proprietary models like GPT are known for their robust performance across various languages, open-source large language models may exhibit variable performance levels. This inconsistency can affect enterprises aiming to deploy language models in multilingual environments, necessitating additional effort to ensure adequate language support.

5. Deployment complexity:

Deploying publically available language models, especially at scale, involves complex technical challenges. These range from infrastructure considerations to optimizing model performance, requiring significant technical expertise and resources to overcome.

6. Uncertainty and risk:

Relying solely on one type of model, whether open or closed source, introduces risks such as the potential for unexpected updates by the provider that could affect model behavior or compliance with regulatory standards.

7. Legal and ethical considerations:

Deploying LLMs entails navigating legal and ethical considerations, from ensuring compliance with data protection regulations to addressing the potential impact of AI on customer experiences. Enterprises must consider these factors to avoid legal repercussions and maintain trust with their users.

8. Lack of public examples:

The scarcity of publicly available case studies on the deployment of publically available LLMs in enterprise settings makes it challenging for organizations to gauge the effectiveness and potential return on investment of these models in similar contexts.

Overall, while there are significant potential benefits to using publically available language models in enterprise settings, including cost savings and the flexibility to fine-tune models, addressing these challenges is critical for successful deployment

Embracing open-source LLMs: A path to innovation and flexibility

In conclusion, open-source language models represent a pivotal shift towards more accessible, customizable, and cost-effective AI solutions for enterprises. They offer a unique blend of benefits, including significant cost savings, enhanced data control, and the ability to tailor AI tools to specific business needs, while also presenting challenges such as the need for customization and navigating support complexities.

Through the collaborative efforts of the global open-source community and the innovative use of these models across various industries, enterprises are finding new ways to leverage AI for growth and efficiency.

However, success in this endeavor requires a strategic approach to overcome inherent challenges, ensuring that businesses can fully harness the potential of publically available LLMs to drive innovation and maintain a competitive edge in the fast-evolving digital landscape.

Large Language Models have surged in popularity due to their remarkable ability to understand, generate, and interact with human language with unprecedented accuracy and fluency.

This surge is largely attributed to advancements in machine learning and the vast increase in computational power, enabling these models to process and learn from billions of words and texts on the internet.

OpenAI significantly shaped the landscape of LLMs with the introduction of GPT-3.5, marking a pivotal moment in the field. Unlike its predecessors, GPT-3.5 was not fully open-source, giving rise to closed-source large language models.

This move was driven by considerations around control, quality, and the commercial potential of such powerful models. OpenAI’s approach showcased the potential for proprietary models to deliver cutting-edge AI capabilities while also igniting discussions about accessibility and innovation.

The introduction of open-source LLM 

Contrastingly, companies like Meta and Mistral have opted for a different approach by releasing models like LLaMA and Mistral as open-source.

These models not only challenge the dominance of closed-source models like GPT-3.5 but also fuel the ongoing debate over which approach—open-source or closed-source—yields better results. Read more

By making their models openly available, Meta and similar entities encourage widespread innovation, allowing researchers and developers to improve upon these models, which in turn, has seen them topping performance leaderboards.

From an enterprise standpoint, understanding the differences between open-source LLM and closed-source LLM is crucial. The choice between the two can significantly impact an organization’s ability to innovate, control costs, and tailor solutions to specific needs.

Let’s dig in to understand the difference between Open-Source LLM and Closed Source LLM

What are open-source large language models?

Open-source large language models, such as the ones offered by Meta AI, provide a foundational AI technology that can analyze and generate human-like text by learning from vast datasets consisting of various written materials.

As open-source software, these language models have their source code and underlying architecture publicly accessible, allowing developers, researchers, and enterprises to use, modify, and distribute them freely.

Let’s dig into different features of open-sourced large language models

1. Community contributions

• Broad participation:

  Open-source projects allow anyone to contribute, from individual hobbyists to researchers and developers from various industries. This diversity in the contributor base brings a wide array of perspectives, skills, and needs into the project.

• Innovation and problem-solving:

  Different contributors may identify unique problems or have innovative ideas for applications that the original developers hadn’t considered. For example, someone might improve the model’s performance on a specific language or dialect, develop a new method for reducing bias, or create tools that make the model more accessible to non-technical users.

2. Wide range of applications

• Specialized use cases:

  Contributors often adapt and extend open-source models for specialized use cases. For instance, a developer might fine-tune a language model on legal documents to create a tool that assists in legal research or on medical literature to support healthcare professionals.

• New features and enhancements:

  Through experimenting with the model, contributors might develop new features, such as more efficient training algorithms, novel ways to interpret the model’s outputs, or integration capabilities with other software tools.

3. Iterative improvement and evolution

• Feedback loop:

  The open-source model encourages a cycle of continuous improvement. As the community uses and experiments with the model, they can identify shortcomings, bugs, or opportunities for enhancement. Contributions addressing these points can be merged back into the project, making the model more robust and versatile over time.

• Collaboration and knowledge sharing:

  Open-source projects facilitate collaboration and knowledge sharing within the community. Contributions are often documented and discussed publicly, allowing others to learn from them, build upon them, and apply them in new contexts.

4. Examples of open-sourced large language models

• Meta’s LLaMA 2
• Bloom by Hugging Face
• Mixtral of Experts by Mistral

What are closed-source large language models?

Closed-source large language models, such as GPT-3.5 by OpenAI, embody advanced AI technologies capable of analyzing and generating human-like text through learning from extensive datasets.

Unlike their open-source counterparts, the source code and architecture of closed-source language models are proprietary, accessible only under specific terms defined by their creators. This exclusivity allows for controlled development, distribution, and usage.

Features of closed-sourced large language models

1. Controlled quality and consistency

• Centralized development: Closed-source projects are developed, maintained, and updated by a dedicated team, ensuring a consistent quality and direction of the project. This centralized approach facilitates the implementation of high standards and systematic updates.
• Reliability and stability: With a focused team of developers, closed-source LLMs often offer greater reliability and stability, making them suitable for enterprise applications where consistency is critical.

2. Commercial support and innovation

• Vendor support: Closed-source models come with professional support and services from the vendor, offering assistance for integration, troubleshooting, and optimization, which can be particularly valuable for businesses.
• Proprietary innovations:  The controlled environment of closed-source development enables the introduction of unique, proprietary features and improvements, often driving forward the technology’s frontier in specialized applications.

3. Exclusive use and intellectual property

• Competitive advantage: The proprietary nature of closed-source language models allows businesses to leverage advanced AI capabilities as a competitive advantage, without revealing the underlying technology to competitors.
• Intellectual property protection: Closed-source licensing protects the intellectual property of the developers, ensuring that their innovations remain exclusive and commercially valuable.

4. Customization and integration

• Tailored solutions: While customization in closed-source models is more restricted than in open-source alternatives, vendors often provide tailored solutions or allow certain levels of configuration to meet specific business needs.
• Seamless integration: Closed-source large language models are designed to integrate smoothly with existing systems and software, providing a seamless experience for businesses and end-users.

Examples of closed-source large language Models

1. GPT 3.5 by OpenAI
2. Gemini by Google
3. Claude by Anthropic

Read: Should Large Language Models be Open-Sourced? Stepping into the Biggest Debates

Open-source and closed-source language models for enterprise adoption:

In terms of enterprise adoption, comparing open-source and closed-source large language models involves evaluating various factors such as costs, innovation pace, support, customization, and intellectual property rights. While I can’t directly access external sources like the VentureBeat article you mentioned, I can provide a general comparison based on known aspects of how enterprises use these models:

Costs

• Open-Source: Generally offers lower initial costs since there are no licensing fees for the software itself. However, enterprises may incur costs related to infrastructure, development, and potentially higher operational costs due to the need for in-house expertise to customize, maintain, and update the models.
• Closed-Source: Often involves licensing fees, subscription costs, or usage-based pricing, which can predictably scale with use. While the initial and ongoing costs can be higher, these models frequently come with vendor support, reducing the need for extensive in-house expertise and potentially lowering overall maintenance and operational costs.

Innovation and updates

• Open-Source: The pace of innovation can be rapid, thanks to contributions from a diverse and global community. Enterprises can benefit from the continuous improvements and updates made by contributors. However, the direction of innovation may not always align with specific enterprise needs.
• Closed-Source: Innovation is managed by the vendor, which can ensure that updates are consistent and high-quality. While the pace of innovation might be slower compared to the open-source community, it’s often more predictable and aligned with enterprise needs, especially for vendors closely working with their client base.

Support and reliability

• Open-Source: Support primarily comes from the community, forums, and potentially from third-party vendors offering professional services. While there can be a wealth of shared knowledge, response times and the availability of help can vary.
• Closed-Source: Typically comes with professional support from the vendor, including customer service, technical support, and even dedicated account management. This can ensure reliability and quick resolution of issues, which is crucial for enterprise applications.

Customization and flexibility

• Open-Source: Offer high levels of customization and flexibility, allowing enterprises to modify the models to fit their specific needs. This can be particularly valuable for niche applications or when integrating the model into complex systems.
• Closed-Source: Customization is usually more limited compared to open-source models. While some vendors offer customization options, changes are generally confined to the parameters and options provided by the vendor.

Intellectual property and competitive advantage

• Open-Source: Using open-source models can complicate intellectual property (IP) considerations, especially if modifications are shared publicly. However, they allow enterprises to build proprietary solutions on top of open technologies, potentially offering a competitive advantage through innovation.
• Closed-Source: The use of closed-source models clearly defines IP rights, with enterprises typically not owning the underlying technology. However, leveraging cutting-edge, proprietary models can provide a different type of competitive advantage through access to exclusive technologies.

Choosing Between Open-Source LLMs and Closed-Source LLMs

The choice between open-source and closed-source language models for enterprise adoption involves weighing these factors in the context of specific business objectives, resources, and strategic directions.

Open-source models can offer cost advantages, customization, and rapid innovation but require significant in-house expertise and management. Closed-source models provide predictability, support, and ease of use at a higher cost, potentially making them a more suitable choice for enterprises looking for ready-to-use, reliable AI solutions.

Large language models hold the promise of transforming multiple industries, but they come with a set of potential risks. These risks of large language models include subjectivity, bias, prompt vulnerabilities, and more.  

In this blog, we’ll explore these challenges and present best practices to mitigate them, covering the use of guardrails, defensive UX design, LLM caching, user feedback, and data selection for fair and equitable results. Join us as we navigate the landscape of responsible LLM deployment. 

Key challenges of large language models

First, let’s start with some key challenges of LLMs that are concerning.  

• Subjectivity of Relevance for Human Beings: LLMs are trained on massive datasets of text and code, but these datasets may not reflect the subjective preferences of all human beings. This means that LLMs may generate content that is not relevant or useful to all users. 

• Bias Arising from Reinforcement Learning from Human Feedback (RHLF): LLMs are often trained using reinforcement learning from human feedback (RHLF). However, human feedback can be biased, either intentionally or unintentionally. This means that LLMs may learn biased policies, which can lead to the generation of biased content. 
• Prompt Leaking: Prompt leaking occurs when an LLM reveals its internal prompt or instructions to the user. This can be exploited by attackers to gain access to sensitive information. 
• Prompt Injection: Prompt injection occurs when an attacker is able to inject malicious code into an LLM’s prompt. This can cause the LLM to generate harmful content. 
• Jailbreaks: A jailbreak is a successful attempt to trick an LLM into generating harmful or unexpected content. This can be done by providing the LLM with carefully crafted prompts or by exploiting vulnerabilities in the LLM’s code. 
• Inference Costs: Inference cost is the cost of running a language model to generate text. It is driven by several factors, including the size, the complexity of the task, and the hardware used to run the model.  

Curious about LLMs, their risks and how they are reshaping the future? Tune in to our Future of Data and AI podcast now!

Quick quiz

Test your knowledge of large language models

LLMs are typically very large and complex models, which means that they require a lot of computational resources to run. This can make inference costs quite high, especially for large and complex tasks. For example, the cost of running a single inference on GPT-3, a large LLM from OpenAI, is currently around $0.06. 

• Hallucinations: There are several factors that can contribute to hallucinations in LLMs, including the limited contextual understanding of LLMs, noise in the training data, and the complexity of the task. Hallucinations can also be caused by pushing LLMs beyond their capabilities. Read more 

Other potential risks of LLMs include privacy violations and copyright infringement. These are serious problems that companies need to be vary of before implementing LLMs. Listen to this talk to understand how these challenges plague users as well as pose a significant threat to society.

Thankfully, there are several measures that can be taken to overcome these challenges.  

Best practices to mitigate these challenges 

Here are some best practices that can be followed to overcome the potential risks of LLMs. 

1. Using guardrails 

Guardrails are technical mechanisms that can be used to prevent large language models from generating harmful or unexpected content. For example, guardrails can be used to prevent LLMs from generating content that is biased, offensive, or inaccurate. 

Guardrails can be implemented in a variety of ways. For example, one common approach is to use blacklists and whitelists. Blacklists are lists of words and phrases that a language model is prohibited from generating. Whitelists are lists of words and phrases that the large language model is encouraged to generate. 

Another approach to guardrails is to use filters. Filters can be used to detect and remove harmful content from the model’s output. For example, a filter could be used to detect and remove hate speech from the LLM’s output. 

2. Defensive UX 

Defensive UX is a design approach that can be used to make it difficult for users to misuse LLMs. For example, defensive UX can be used to make it clear to users that LLMs are still under development and that their output should not be taken as definitive. 

One way to implement defensive UX is to use warnings and disclaimers. For example, a warning could be displayed to users before they interact with it, informing them of the limitations of large language models and the potential for bias and error. 

Another way to implement defensive UX is to provide users with feedback mechanisms. For example, a feedback mechanism could allow users to report harmful or biased content to the developers of the LLM. 

3. Using LLM caching 

LLM caching reduces the risk of prompt leakage by isolating user sessions and temporarily storing interactions within a session, enabling the model to maintain context and improve conversation flow without revealing specific user details.  

This improves efficiency, limits exposure to cached data, and reduces unintended prompt leakage. However, it’s crucial to exercise caution to protect sensitive information and ensure data privacy when using large language models. 

4. User feedback 

User feedback can be used to identify and mitigate bias in LLMs. It can also be used to improve the relevance of LLM-generated content. 

One way to collect user feedback is to survey users after they have interacted with an LLM. The survey could ask users to rate the quality of the LLM’s output and identify any biases or errors. 

Another way to collect user feedback is to allow users to provide feedback directly to the developers of the LLM. This feedback could be provided via a feedback form or a support ticket. 

5. Using data that promotes fairness and equality 

It is of paramount importance for machine learning models, particularly Large Language Models, to be trained on data that is both credible and advocates fairness and equality.

Credible data ensures the accuracy and reliability of model-generated information, safeguarding against the spread of false or misleading content. 

To do so, training on data that upholds fairness and equality is essential to minimize biases within LLMs, preventing the generation of discriminatory or harmful outputs, promoting ethical responsibility, and adhering to legal and regulatory requirements.  

Overcome the risks of large language models

In conclusion, Large Language Models (LLMs) offer immense potential but come with inherent risks, including subjectivity, bias, prompt vulnerabilities, and more.  

This blog has explored these challenges and provided a set of best practices to mitigate them.

These practices encompass implementing guardrails to prevent harmful content, utilizing defensive user experience (UX) design to educate users and provide feedback mechanisms, employing LLM caching to enhance user privacy, collecting user feedback to identify and rectify bias, and, most crucially, training LLMs on data that champions fairness and equality.  

By following these best practices, we can navigate the landscape of responsible LLM deployment, promote ethical AI development, and reduce the societal impact of biased or unfair AI systems. 

If you’re interested to learn large language models (LLMs), you’re in the right place. LLMs are all the rage these days, and for good reason. They’re incredibly powerful tools that can be used to do a wide range of things, from generating text to translating languages to writing code.

LLMs can be used to build a variety of applications, such as chatbots, virtual assistants, and translation tools. They can also be used to improve the performance of existing NLP tasks, such as text summarization and machine translation.

In this blog post, we are going to share the top 10 YouTube videos for learning about LLMs. These videos cover everything from the basics of how LLMs work to how to build and deploy your own LLM. Experts in the field teach these concepts, giving you the assurance of receiving the latest information.

1. LLM for real-world Applications

Custom LLMs are trained on your specific data. This means that they can be tailored to your specific needs. For example, you could train a custom LLM on your customer data to improve your customer service experience.

LLMs are a powerful tool that can be used to improve your business in a number of ways. If you’re not already using LLMs in your business, I encourage you to check out the video above to learn more about their potential applications.

In this video, you will learn about the following:

• What are LLMs and how do they work?
• What are the different types of LLMs?
• What are some of the real-world applications of LLMs?
• How can you get started with using LLMs in your own work?

2. Emerging Architectures for LLM Applications

In this video, you will learn about the latest approaches to building custom LLM applications. This means that you can build an LLM that is tailored to your specific needs. You will also learn about the different tools and technologies that are available, such as LangChain.

Applications like Bard, ChatGPT, Midjourney, and DallE have entered some applications like content generation and summarization. However, there are inherent challenges for a lot of tasks that require a deeper understanding of trade-offs like latency, accuracy, and consistency of responses.

Any serious applications of LLMs require an understanding of nuances in how LLMs work, embeddings, vector databases, retrieval augmented generation (RAG), orchestration frameworks, and more.

In this video, you will learn about the following:

• What are the challenges of using LLMs in real-world applications?
• What are some of the emerging architectures for LLM applications?
• How can these architectures be used to overcome the challenges of using LLMs in real-world applications?

3. Vector Similarity Search

This video explains what vector databases are and how they can be used for vector similarity searches. Vector databases are a type of database that stores data in the form of vectors. Vectors are mathematical objects that represent the direction and magnitude of a force or quantity.

A vector similarity search is the process of finding similar vectors in a vector database. Vector similarity search can be used for a variety of tasks, such as image retrieval, text search, and recommendation systems.

In this video, you will learn about the following:

• What are vector databases?
• What is vector similarity search?
• How can vector databases be used for vector similarity searches?
• What are some of the benefits of using vector databases for vector similarity searches?

4. Agents in LangChain

This video explains what LangChain agents are and how they can be used to build AI applications. LangChain agents are a type of artificial intelligence that can be used to build AI applications. They are based on large language models (LLMs), which are a type of artificial intelligence that can generate and understand human language.

Link to video – Agents in LangChain

In this video, you will learn about the following:

• What are LangChain agents?
• How can LangChain agents be used to build AI applications?
• What are some of the benefits of using LangChain agents to build AI applications?

5. Build your own ChatGPT

This video shows how to use the ChatGPT API to build your own AI application. ChatGPT is a large language model (LLM) that can be used to generate text, translate languages, and answer questions in an informative way.

Link to video: Build your own ChatGPT

In this video, you will learn about the following:

• What is the ChatGPT API?
• How can the ChatGPT API be used to build AI applications?
• What are some of the benefits of using the ChatGPT API to build AI applications?

6. The Power of Embeddings with Vector Search

Embeddings are a powerful tool for representing data in an easy-to-understand way for machine learning algorithms. Vector search is a technique for finding similar vectors in a database. Together, embeddings and vector search can be used to solve a wide range of problems, such as image retrieval, text search, and recommendation systems.

Key learning outcomes:

• What are embeddings and how do they work?
• What is vector search and how is it used?
• How can embeddings and vector search be used to solve real-world problems?

7. AI in Emergency Medicine

Artificial intelligence (AI) is rapidly transforming the field of emergency medicine. AI is being used to develop new diagnostic tools, improve the efficiency of care delivery, and even predict patient outcomes.

Key learning outcomes:

• What are the latest advances in AI in emergency medicine?
• How is AI being used to improve patient care?
• What are the challenges and opportunities of using AI in emergency medicine?

8. Generative AI Trends, Ethics, and Societal Impact

Generative AI is a type of AI that can create new content, such as text, images, and music. Generative AI is rapidly evolving and has the potential to revolutionize many industries. However, it also raises important ethical and societal questions.

Key learning outcomes:

• What are the latest trends in generative AI?
• What are the potential benefits and risks of generative AI?
• How can we ensure that generative AI is used responsibly and ethically?

9. Hugging Face + LangKit

Hugging Face and LangKit are two popular open-source libraries for natural language processing (NLP). Hugging Face provides a variety of pre-trained NLP models, while LangKit provides a set of tools for training and deploying NLP models.

Key learning outcomes:

• What are Hugging Face and LangKit?
• How can Hugging Face and LangKit be used to build NLP applications?
• What are some of the benefits of using Hugging Face and LangKit?

10. Master ChatGPT for Data Analysis and Visualization!

ChatGPT is a large language model that can be used for a variety of tasks, including data analysis and visualization. In this video, you will learn how to use ChatGPT to perform common data analysis tasks, such as data cleaning, data exploration, and data visualization.

Key learning outcomes:

• How to use ChatGPT to perform data analysis tasks
• How to use ChatGPT to create data visualizations
• How to use ChatGPT to communicate your data findings

Visit our YouTube channel to learn large language model

LLMs can help you build your own large language models, like ChatGPT. They can also help you use custom language models to grow your business. For example, you can use custom language models to improve customer service, develop new products and services, automate marketing and sales tasks, and improve the quality of your content.

So, what are you waiting for? Start learning about LLMs today!