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Spring of Realtime LLMs
  • Get started with the Bootcamp!
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Bootcamp Introductory Session | National Science Day
  • 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 Section: Overview of the Transformers Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformers Architecture
      • Vision Transformers
    • Graded Quiz 1
  • Prompt Engineering and Token Limits (Early Access)
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow
    • Token Limits and Hallucinations
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task for 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 the 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)
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • 1 – Dropbox Retrieval App
      • Understanding Docker
      • Build the Dockerized App
      • Retrofitting our Dropbox app
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – RAG with Open Source and Running "Examples"
    • 4 (Bonus Section) – Realtime RAG with LlamaIndex and Pathway
      • Understanding the Basics
      • Implementation
      • Sample Business Use-case
  • Bonus Resource: Recorded Interactions from the Archives
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Form for Submission
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  • Module Release Timelines and the Coursework:
  • Note:
  • What are Bonus Sections/Resources?

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  1. Get started with the Bootcamp!

Course Syllabus and Timelines

PreviousCourse StructureNextKnow your Educators

Last updated 1 year ago

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By the end of this course, you will:

  • Be proficient in developing LLM-based applications for production applications from day 0.

  • Have a clear understanding of LLM architecture and pipeline.

  • Be able to perform prompt engineering to use generative AI tools such as ChatGPT.

  • Create an open-source project on a real-time stream of data or static data.

Module Release Timelines and the Coursework:

Module
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Module
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Note:

Once the problem statements and the hands-on development module is released on the 12th of March 2024, the project submissions will be open till 25th March 2024, Monday 12 am IST.

What are Bonus Sections/Resources?

Throughout the bootcamp, you'll come across various modules or links labeled as bonus resources. These are not compulsory for building a project by the end of the bootcamp or for attempting the quizzes. Nonetheless, they are high-quality resources that could enhance your understanding, although they might require additional prerequisites. Depending on your starting point and the pace at which you're progressing through the bootcamp, you can explore or bypass these bonus materials.

1 – Basics of LLMs

  • What is generative AI and how it's different

  • Understanding LLMs

  • Advantages and Common Industry Applications

  • Bonus section: Google Gemini and Multimodal LLMs

--- Release date: 6 March '24

2 – Word Vectors

  • What are word vectors and word-vector relationships?

  • Role of context

  • Transforming vectors in LLM responses

  • Overview of Transformers Architecture

  • Bonus Resource: Transformers Architecture, Self-attention, Multi-head attention, and Vision Transformers

--- Release date: 7 March '24

3 – Prompt Engineering

  • Introduction and in-context learning

  • Best practices to follow: Few Shot Prompting and more

  • Token Limits

  • Prompt Engineering Exercise (Ungraded)

--

Release date: 8 March '24

Refresher Module

  • Overview of learnings so far sent over registered email address.

  • Release of bootcamp keynote session(s).

4 – RAG and LLM Architecture

  • Introduction to RAG

  • LLM Architecture Used by Enterprises

  • RAG vs Fine-Tuning and Prompt Engineering

  • Key Benefits of RAG for Realtime Applications

  • Bonus: Similarity Search for Efficient Information Retrieval

  • Bonus: Use of LSH + kNN and Incremental Indexing

  • Bonus: Forgetting in LLMs and Stream Data Processing (archived live interactions)

-- Release date: 10th March '24

5 – Hands-on Project

  • Installing Dependencies and Pre-requisites

  • Building a Dropbox RAG App using open-source

  • Building Realtime Discounted Products Fetcher for Amazon Users

  • Problem Statements for Projects

  • Project Submission

-- Release date: 14th March '24 (Delayed)