Large Language Models Bootcamp


.  5 DAYS . 40 HOURS .


Comprehensive, hands-on curriculum to give you a headstart in building Large Language Model Applications.
LLM Models


30% OFF

4.95 · 640+ reviews



Software subscriptions and cloud services credit up to USD $500.
Breakfast, lunch and beverages daily.
Networking dinner.
Course material and access to coding labs, Jupyter notebooks, and hundreds of learning resources for 1 year.

Countdown to Washington D.C. LLM Bootcamp



Learn From Industry Leaders


Learn to Build and Deploy Custom LLM Applications

Pre-trained Large Language Models like ChatGPT offer impressive capabilities but they cannot be used in scenarios where the underlying data is proprietary and requires industry-specific knowledge. Businesses are rushing to build custom LLM applications that offer enhanced performance, control, customization and most importantly, competitive advantage.

This bootcamp offers a comprehensive introduction to get started with building a ChatGPT on your own data. By the end of the bootcamp, you will be capable of building LLM-powered applications on any dataset of your choice.

In collaboration with our delivery partners

Who Is This Course For


Anyone interested in getting a headstart by getting a hands-on experience with building LLM applications.

Data professionals

Data professionals who want to supercharge their data skills using cutting-edge generative AI tools and techniques.

Product leaders

Product leaders at enterprises or startups seeking to leverage LLMs to enhance their products, processes and services.

Curriculum Highlights

Generative AI and LLM Fundamentals

A comprehensive introduction to the fundamentals of generative AI, foundation models and Large language models

Canonical Architectures of LLM Applications

An in-depth understanding of various LLM-powered application architectures and their relative tradeoffs

Embeddings and Vector Databases

Hands-on experience with vector databases and vector embeddings

Prompt Engineering

Practical experience with writing effective prompts for your LLM applications

Orchestration Frameworks: LangChain and Llama Index

Practical experience with orchestration frameworks like LangChain and Llama Index

Deployment of LLM Applications

Learn how to deploy your LLM applications using Azure and Hugging Face cloud

Customizing Large Language Models

Practical experience with fine-tuning, parameter efficient tuning and retrieval parameter-efficient + retrieval-augmented approaches

Building An End-to-End Custom LLM Application

A custom LLM application created on selected datasets


30% OFF

4.95 · 640+ reviews



Software subscriptions and cloud services credit up to USD $500.
Breakfast, lunch and beverages daily.
Networking dinner.
Course material and access to coding labs, Jupyter notebooks, and hundreds of learning resources for 1 year.

Countdown to Washington D.C. LLM Bootcamp


Technologies and Tools

Course Syllabus

DAY 1 - LLM Fundamentals

LLM Fundamentals

Dive into machine learning with a focus on large language models. Learn the differences between traditional programming and ML, understand neural network structures, and explore the evolution and importance of foundation models

  • Traditional Programming Vs Machine Learning
  • Types of Machine Learning
  • Neural Networks Architecture
  • History of Language Models
  • What are large language models
  • What are foundation models

Introduction to Generative AI

Quick overview of generative AI, LLM's, and foundation models. Learn more about how transformers and attention mechanism works behind text and image based models.

  • Types of generative AI models
    • Text based models
    • Image based models
  • Intro to image generation
    • Diffusion models
  • Generative AI applications

Evolution of Embeddings - The Building Blocks of Large Language Models

In this module, we will be reviewing how embeddings have evolved from the simplest one-hot encoding approach to more recent semantic embeddings approaches. The module will go over the following topics:

  • Review of classical techniques
    • Review of binary/one-hot, count-based and TF-IDF techniques for vectorization
    • Capturing local context with n-grams and challenges
  • Semantic Encoding Techniques
    • Overview of Word2Vec and dense word embeddings
    • Application of Word2Vec in text analytics and NLP tasks
  • Text Embeddings
    • Word and sentence embeddings
  • Text similarity measures
    • Dot product, Cosine similarity, Inner product

Hands-on Exercise

  • Creating a TF-IDF embeddings on a document corpus
  • Calculating similarity between sentences using cosine similarity and dot product

DAY 2 - Emerging Architectures for LLM Applications

Attention Mechanism and Transformers

Dive into the world of large language models, discovering the potent mix of text embeddings, attention mechanisms, and the game-changing transformer model architecture.

Attention mechanism and transformer models

  • Encoder decoder
  • Transformer networks: tokenization, embedding, positional encoding and transformers block
  • Attention mechanism
  • Self-Attention
  • Multi-head Attention
  • Transformer models

Hands-on Exercise

  • Understanding attention mechanisms: Self-attention for contextual word analysis

Efficient Storage and Retrieval of Vector Embeddings Using Vector Databases

In collaboration with Learn about efficient vector storage and retrieval with vector database, indexing techniques, retrieval methods, and hands-on exercises.

  • Overview
    • Rationale for vector databases
    • Importance of vector databases in LLMs 
    • Popular vector databases
  • Indexing techniques
    • Product Quantization (PQ), Locality Sensitive Hashing (LSH) and Hierarchical Navigable Small World (HNSW)
  • Retrieval techniques
    • Cosine Similarity, Nearest Neighbor Search
  • Hands-on Exercise
    • Creating a vector store using HNSW
    • Creating, storing and retrieving embeddings using cosine similarity and nearest neighbors

Emerging Architectures for Large Language Model Applications

In this module we will understand the common use cases of large language models and fundamental building blocks of such applications. Learners will be introduced to the following topics at a very high level without going into the technical details:

  • Large language models and foundation models
  • Prompts and prompt engineering
  • Context window and token limits
  • Embeddings and vector databases
  • Build custom LLM applications by:
    • Training a new model from scratch
    • Fine-tuning foundation LLMs
    • In-context learning
  • Canonical architecture for and end-to-end LLM application

DAY 3 - Prompt Engineering & Fine-tuning LLMs

Leveraging Text Embeddings for Semantic Search

Understand how semantic search overcomes the fundamental limitation in lexical search i.e. lack of semantic . Learn how to use embeddings and similarity in order to build a semantic search model.

  • Understanding and Implementing Semantic Search
    • Introduction and importance of semantic search
    • Distinguishing Semantic Search from lexical search
    • Semantic search using text embeddings
  • Exploring Advanced Concepts and Techniques in Semantic Search
    • Multilingual search
    • Limitations of embeddings and similarity in semantic search
    • Improving semantic search beyond embeddings and similarity
  • Hands-on Exercise
    • Building a simple semantic search engine with multilingual capability

Fundamentals of Prompt Engineering

Unleash your creativity and efficiency with prompt engineering. Seamlessly prompt models, control outputs, and generate captivating content across various domains and tasks.

  • Prompt Design and Engineering
    • Prompting by instruction
    • Prompting by example
  • Controlling the Model Output
    • When to Stop
    • Being Creative vs. Predictable
    • Saving and sharing your prompts
  • Use Case Ideation
    • Utilizing Goal, Task and Domain for perfect prompt
    • Example Use Cases
      • Summarizing (summarizing a technical report)
      • Inferring (sentiment classification, topic extraction)
      • Transforming text (translation, spelling and grammar correction)
      • Expanding (automatically writing emails)
      • Generating a product pitch
      • Creating a business model Canvas
      • Simplifying technical concepts
      • Composing an email

Fine Tuning Foundation LLMs

In collaboration with Discover the ins and outs of fine-tuning foundation language models (LLMs) through theory discussions, exploring rationale, limitations, and Parameter Efficient Fine Tuning.

  • Fine Tuning Foundation LLMs
    • Rationale for fine tuning
    • Limitations of fine tuning
    • Parameter efficient fine tuning
  • Hands-on Exercise:
    • Fine-tuning and deploying OpenAI GPT model on Azure

DAY 4 - LangChain for LLM Application Development

Orchestration Frameworks to Build Applications on Enterprise Data

Explore the necessity of orchestration frameworks, tackling issues like foundation model retraining, token limits, data source connectivity, and boilerplate code. Discover popular frameworks, their creators, and open source availability.

  • Why are Orchestration Frameworks Needed?
    • Eliminate the need for foundation model retraining
    • Overcoming token limits
    • Connecters for data sources.

LangChain for LLM Application Development

In collaboration with Build LLM Apps using LangChain. Learn about LangChain's key components such as Models, Prompts, Parsers, Memory, Chains, and Question-Answering. Get hands-on evaluation experience.

  • Introduction to LangChain
    • Schema, Models, and Prompts
    • Memory, Chains
  • Loading, Transforming, Indexing, and Retrieving data
    • Document loader
    • Text splitters
    • Retrievers
  • LangChain Use Cases
    • Summarization – Summarizing long documents
    • Question & Answering Using Documents As Context
    • Extraction – Getting structured data from unstructured text
    • Evaluation – Evaluating outputs generated from LLM models
    • Querying Tabular Data – without using any extra code
  • Hands-on Exercise: 
    • Using LangChain loader, splitter, retrievals on a pdf document

Autonomous Agents: Delegating Decision Making to AI

Use LLMs to make decisions about what to do next. Enable these decisions with tools. In this module, we’ll talk about agents. We’ll learn what they are, how they work, and how to use them within the LangChain library to superpower our LLMs.

  • Agents and Tools
  • Agent Types
    • Conversational agents
    • OpenAI functions agents
    • ReAct agents
    • Plan and execute agents
  • Hands-on Exercise: Create and execute some of the following agents
    • Excel agent
    • JSON agent
    • Python Pandas agent
    • Document comparison agent
    • Power BI agent

LLMOps : Observability & Evaluation

In collaboration with LLMOps encompasses the practices, techniques and tools used for the operational management of large language models in production environments. LLMs offer tremendous business value, humans are involved in all stages of the lifecycle of an LLM from acquisition of data to interpretation of insights. In this module we will learn about the following:

  • Principles of Responsible AI
    • Fairness and Eliminating Bias
    • Reliability and Safety
    • Privacy and Data Protection
  • Review techniques for assessing large language model applications, including:
    • Model fine-tuning
    • Model inference and serving
    • Model monitoring with human feedback
  • Introduce LangKit by WhyLabs for data-centric LLMOps:
    • Guardrails: Define rules to govern prompts and responses for LLM applications.
    • Evaluation: Assess LLM performance using known prompts to identify issues.
    • Observability: Collect telemetry data about the LLM's internal state for monitoring and issue detection.
  • Hands-on Exercise:
    • Using Langkit Evaluate LLM performance on specific prompts

DAY 5 - Project: Build A Custom LLM Application On Your Own Data

Project: Build A Custom LLM Application On Your Own Data

On the last day of the bootcamp, the learners will apply the concepts and techniques learned during the bootcamp to build an LLM application. Learners will choose to implement one of the following:

  • Virtual Assistant: A dynamic customer service agent designed for the car manufacturing industry.
  • Content Generation (Marketing Co-pilot) : Enhancing your marketing strategies with an intelligent co-pilot.
  • Conversational Agent (Legal and Compliance Assistant): Assisting with legal and compliance matters through interactive conversations.
  • QnA (IRS Tax Bot): An intelligent bot designed to answer your questions about IRS tax-related topics.
  • Content Personalizer: Tailoring content specifically to your preferences and needs.
  • YouTube Virtual Assistant: Engage in interactive conversations with your favorite YouTube channels and playlists.


Attendees will receive the following:

  • Datasets consisting of large number of documents from various industries
  • Detailed instructions for implementation
  • Code templates in Data Science Dojo sandboxes to get you up and running quickly
  • Exclusive access to powerful cloud-based resources and receive your own OpenAI key for hassle-free deployment of your functional project
  • A supportive cohort and course instructors to set you up for success with personalized support, guidance, and prompt answers to your questions throughout the bootcamp.

At the end of the bootcamp, you'll leave with a fully functional LLM application deployed in either Streamlit or Hugging Face cloud. Showcase your achievements and leverage the power of AI to transform your industry, gaining a competitive edge and staying ahead of the curve.

Course Schedule

Daily schedule: 9 am - 5 pm | Breakfast, lunch and beverages | Breakout sessions and in-class activities

Washington D.C. Address October 16 - 20, 2023
Austin November 6 -10, 2023
New York December 4 - 8, 2023
Singapore January 2024
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Frequently Asked Questions

Will I be able to build my own ChatGPT-like application after I finish the bootcamp?


You will leave the bootcamp with your own fully functional LLM application.

Will I receive a certificate?

Yes. You will receive a verified certificate from The University of New Mexico carrying 7 CEUs.

Are there any financing options available?

Yes, you can find out more here.

What software and subscriptions are included in the registration fee?

  1. An assortment of software subscriptions worth $500, enhancing the value of the program.
  2. A 12-month unlimited access to all learning resources, allowing for continuous learning and review.
  3. A repository of practice materials.

Can I use my own dataset for the project in the bootcamp?

Yes, you can. You will also have the option to choose from the various datasets provided for the end-to-end project. 

Are there any prerequisites or recommended prior knowledge for attending the bootcamp?

Yes, you will be provided with the pre-requisite material on our learning platform before the bootcamp.

30% OFF

Join an upcoming cohort

Large Language Models Bootcamp




Washington D.C.


October 16 - 20, 2023


October 15, 2023