Large Language Models Bootcamp


.  5 DAYS . 40 HOURS .


Comprehensive, hands-on curriculum to give you a headstart in building Large Language Model Applications.
LLM Bootcamp | Data Science Dojo


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Ratings | LMM Bootcamp | Data Science Dojo4.95 · 640+ reviews



Software subscriptions and cloud services credit up to USD $500.
Breakfast, lunch and beverages daily.
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

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

Ratings | LMM Bootcamp | Data Science Dojo4.95 · 640+ reviews



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

Countdown to Austin LLM Bootcamp


Technologies and Tools


Our customers and partners love us!
Alessya Visnjic | WhyLabs | Data Science Dojo
WhyLabs | LLM Bootcamp | Data Science Dojo
Partnering with Data Science Dojo aligns with our mission to make data science accessible. Their bootcamps contribute to safe AI deployment education.
Alessya Visnjic
CEO & Co-founder
Jeff Fong | Hover | Data Science Dojo
Hover | LLM Bootcamp | Data Science Dojo
LLM bootcamp provides exceptional, hands-on initiation into LLMs and practical applications. A deep dive into the subject with theoretical knowledge.
Jeff Fong
Principal Product Manager
Abrar | IFC | LLM Bootcamp | Data Science Dojo
ICF | LLM Bootcamp | Data Science Dojo
Six months of course material access post-training is valuable. Instructors ensure success, making it a top-notch learning experience in AI.
Abrar Bhuiyan
Supervisor, Software Application Engineer
Fatemeh Shah Mohammadi | Health University of Utah | Data Science Dojo
University of Utah | LLM Bootcamp | Data Science Dojo
Instructors simplified complex topics effectively. Hands-on learning enhanced my LLM understanding in 5 days. Intensive but immeasurable rewards.
Fatemeh Shah-Muhammadi
Research Assistant Professor
Sage Elliott | WhyLabs | Data Science Dojo
WhyLabs | LLM Bootcamp | Data Science Dojo
A rewarding opportunity to help students of all backgrounds learn new techniques. Being part of a passionate data science community was a real privilege.
Sage Elliot
AI Engineer | MLOps & Robotics
Bernard Tumanjong | United States Space Force | Data Science Dojo
US Space Force | LLM Bootcamp | Data Science Dojo
LLM Bootcamp provides hands-on experience with expert instructors, a comprehensive framework, and extensive resources for substantial upskilling.
Bernard Tumanjong
Chief, Operations Analysis
Fahad Alswaina | KAPSARC | Data Science Dojo
KAPSARC | LLM Bootcamp | Data Science Dojo
The effort in providing resources was commendable. Teachers and assistants were helpful, making it one of the best courses. I learned a lot for future use.
Fahad Alswaina
Sr. Data and Model Solutions Associate
Kevin Song | ICF | Data Science Dojo
ICF | LLM Bootcamp | Data Science Dojo
Top-notch speakers, hands-on workshops, and networking make it a wonderful tech experience. Raja's in-depth teaching focuses on learning concepts.
Kevin Song
Software Engineer
Taimur Rashid | Redis | Data Science Dojo
Weaviate | LLM Bootcamp | Data Science Dojo
Comprehensive curriculum in generative AI, prompt engineering, and data retrieval. Excited about practical training opportunities.
Taimur Rashid
Instructor Harrison Chase from LangChain, guiding participants through the LLM Bootcamp.
LangChain | LLM Bootcamp | Data Science Dojo
Collaborating with Data Science Dojo nurtures the next generation of LLM developers, commendable for fostering a dedicated creator community.
Harrison Chase
Co-Founder and CEO
Luis Armando M | Raisa | Data Science Dojo
Radiacion Aplicada A La Industria | Data Science Dojo
This boot camp provided great content, equipping me with skills and confidence for efficient job execution. Enjoyable class and projects, a rewarding decision.
Luis Armando M
Sales and Services Manager
Amity Fox | Strategic Insights L.L.C | Data Science Dojo
343 Industries | Data Science Dojo
The LLM Bootcamp offered an enlightening, well-structured learning experience, invaluable networking, and insights into real-world AI challenges.
Amity Fox
Data Scientist
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Course Syllabus

DAY 1 - LLM Fundamentals

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

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

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

DAY 2 - Vector Databases and Prompt Engineering

Efficient Storage and Retrieval of Vector Embeddings Using Vector Databases

In collaboration with Redis | LLM Bootcamp | Data Science Dojo 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

Leveraging Text Embeddings for Semantic Search

In collaboration with Redis| LLM Bootcamp | Data Science Dojo 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

DAY 3 - Fine-tuning LLMs

Fine Tuning Foundation LLMs

In collaboration with Runpod| LLM Bootcamp | Data Science Dojo In-depth discussion on fine-tuning of large language models through theory discussions, exploring rationale, limitations, and Parameter Efficient Fine Tuning.

  • Fine Tuning Foundation LLMs
    • RLHF, Transfer learning and Fine tuning
    • Limitations for fine tuning
    • Parameter efficient fine tuning
    • Quantization of LLMs
    • Low Rank Adaptation (LoRA) and QLoRA
    • Fine tuning vs. RAG: When to use one or the other. Risks and limitations.
  • Hands-on Exercise:
    • In-Class: Instruction fine-tuning, deploying and evaluating a LLaMA2-7B 4-bit quantized model
    • Homework: Fine-tuning and deploying OpenAI GPT model on Azure

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 LangChain | LLM Bootcamp | Data Science Dojo 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

DAY 4 - LangChain for LLM Application Development

Autonomous Agents: Delegating Decision Making to AI

In collaboration with LangChain | LLM Bootcamp | Data Science Dojo 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 WhyLabs | LLM Bootcamp | Data Science Dojo 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

Evaluating Large Language Models (LLMs)

Dive into Large Language Model (LLM) evaluation, examining its importance, common issues, and key metrics such as BLEU and ROUGE, and apply these insights through a hands-on summarization exercise.

Introduction to LLM Evaluation

  • What is evaluation and why is it important for LLMs?
  • Overview of common mistakes made by LLMs
  • Brief introduction to benchmark datasets and metrics

Evaluation Metrics

  • Explain commonly used automatic metrics (BLEU, ROUGE, BERTScore)
  • Compare strengths and weaknesses of different metrics
  • Discuss role of human evaluation and techniques (Likert scale)

Hands-on Exercise

  • Evaluating LLMs summarization using metrics like Rouge and Bertscore

Productionize your LLM application

In collaboration with ZenML| LLM Bootcamp | Data Science Dojo This module covers how to scale and automate LLM applications using ZenML. ZenML streamlines data versioning, caching, deployment, and collaboration for efficient LLM app development.

Key Challenges in building Enterprise-Level LLM Apps

  • Introduction about challenges in production
  • Data Versioning in production
  • Overview of ZenML's role in scaling LLM apps
  • Dashboard access for tracking pipeline progress and data storage

Hands-On Exercise

  • Create a QnA agent with ZenML pipelines

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

Seattle Address April 22 - 26, 2024
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Frequently Asked Questions

Are there any prerequisites?

Yes, a very basic level of python programming language.

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.

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.

Will there be future bootcamps in other locations or online?

Data Science Dojo has conducted bootcamps in various cities in the past and plans to continue expanding to other locations. They are also exploring options for online bootcamps.

Do I need to bring my own laptop? Can we be provided with a device/platform where we can do the assignments?

Yes, you will need to bring your laptop. As for software installation, we use browser-based coding labs. You will not need to install any software on your laptop. 

Can I fine-tune the LLM model on my own custom data?

Yes, you can fine-tune the LLM model on your own custom data sources. The bootcamp will provide guidance on how to add custom data sources and fine-tune the model to answer specific questions related to those sources. However, please make sure to get the dataset reviewed before the bootcamp starts to avoid any last-minute inconveniences.  

Can I get a certificate of completion after the bootcamp?

Yes, participants who successfully complete the bootcamp will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and demonstrate your expertise in building large language model applications.  

Will I have access to the learning platform after the program ends?

Yes, the participants will have access to the learning platform for 12 months after the bootcamp ends. 

Can I get support and guidance after the bootcamp?

Yes, Data Science Dojo provides ongoing support and guidance to bootcamp participants even after the program ends. This includes access to a community of fellow participants and instructors who can help answer questions and provide further assistance.  

If I have questions during the live instructor-led sessions or while working on homework?

Yes, our live instructor-led sessions are interactive. During these sessions, students are encouraged to ask questions, and our instructors respond without rushing. Additionally, discussions within the scope of the topic being taught are actively encouraged. We understand that questions may arise during homework, and to assist with that, we offer office hours to help unblock students between sessions. Rest assured, you won't have to figure everything out by yourself – we are committed to providing the support you need for a successful learning experience.

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Large Language Models Bootcamp

April 22 - 26, 2024
April 21, 2024