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RAG and LLM Bootcamp
  • Welcome to the Bootcamp
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Kick-Off Session for the Bootcamp
  • Basics of LLMs
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of LLMs
    • Bonus Resource: Multimodal LLMs and Google Gemini
  • Word Vectors, Simplified
    • What is a Word Vector?
    • Word Vector Relationships
    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
    • Bonus: Overview of the Transformer Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformer Architecture
      • Vision Transformers (ViTs)
    • Bonus: Future of LLMs? | By Transformer Co-inventor
    • Graded Quiz 1
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • For Starters: Best Practices
    • Navigating Token Limits
    • Hallucinations in LLMs
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task fror the Module
  • RAG and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)?
    • Primer to RAG: Pre-trained and Fine-Tuned LLMs
    • In-context Learning
    • High-level LLM Architecture Components for In-context Learning
    • Diving Deeper: LLM Architecture Components
    • Basic RAG Architecture with Key Components
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in RAG
    • Key Benefits of using RAG in an Enterprise/Production Setup
    • Hands-on Demo: Performing Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search (Bonus Module)
    • Bonus Video: Implementing End-to-End RAG | 1-Hour Session
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites (Must)
    • Docker Basics
    • Your Hands-on RAG Journey
    • 1 – First RAG Pipeline
      • Building with Open AI
      • How it Works
      • Using Open AI Alternatives
      • RAG with Open Source and Running "Examples"
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – Private RAG with Mistral, Ollama and Pathway
      • Building a Private RAG project
      • (Bonus) Adaptive RAG Overview
    • 4 – Realtime RAG with LlamaIndex/Langchain and Pathway
      • Understand the Basics
      • Implementation with LlamaIndex and Langchain
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Sample Projects and Additional Resources
    • Submit Project for Review
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  • Course Format
  • Course Timelines
  • Course Completion Criteria and Prizes
  1. Welcome to the Bootcamp

Course Structure

PreviousWelcome to the BootcampNextCourse Syllabus and Timelines

Last updated 11 months ago

The are seasoned professionals with rich academic and applied research backgrounds. Furthermore, we have members from the Developers' Society at BITS Goa and Pathway who have hands-on LLM app development experiences.

However, the curriculum of this course goes beyond conventional AI studies to prioritize the development of real-time Retrieval Augmented Generation (RAG) applications – a foundational technique needed by companies trying to leverage Generative AI in practical settings. This focus tackles two prevalent industry hurdles that even experienced professionals find intricate: the application of Generative AI in actual production environments and the crafting of real-time solutions for industry use cases.

By the end of this program, you will have not only acquired the knowledge to create impactful, open-source LLM applications using RAG and live data streams but also completed a significant personal project. This opportunity allows students to dive deep into and excel in a technically demanding yet immensely rewarding emerging technological field.

Course Format

The introductory segment of this course has been made available since 8th June 2024. Registration remains open for the first 2 weeks, providing ample time to catch up with the initial, more straightforward modules. This approach ensures you're well-prepared before diving into relatively complex RAG-specific and hands-on development modules.

To complete the bootcamp, you must finish all quizzes by the established deadlines and submit your final project. You have the option to work on the project either solo or in a team of two. If you decide to team up closer to the project submission, choose a partner with skills that complement yours, though you are equally encouraged to take on this challenge by yourself. In cases with a team of two, the prizes mentioned below will be awarded to both members.

Your project should involve creating and sharing an innovative GitHub project that utilizes the open-source RAG frameworks discussed in the coursework to tackle real-world challenges. More resources for bootcamp completion and eligibility for the top 6 teams will be discussed as the hands-on module approaches.

Rewards for Completing the Bootcamp:

  • For All Completers: Certificates, T-shirts, and swag will be given to you upon course completion.

  • For the Top 6 Teams: You will receive XBOX controllers, phone camera lenses, and JBL waterproof speakers as special prizes. The top projects will also be featured on Pathway's official blog, becoming a valuable credential in your professional journey.

A Piece of Advice

For those new to creating real-world AI applications, be ready to face challenges in selecting problems, integrating data, and applying foundational LLM knowledge. Engaging early is crucial to navigate these challenges successfully.

In brief – it's primarily recorded. Designed with a blend of learning styles in mind, the course predominantly features pre-recorded lectures, enabling you to progress at their convenience. Moreover, interactive live sessions will be scheduled, and registered attendees () will be informed beforehand. Thus, please ensure you've registered with an email address you access regularly and use for receiving calendar invites.

Course Timelines

Note: Module-specific released dates are specified after mentioning the ahead.

Course Completion Criteria and Prizes

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