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Course Syllabus and Timelines

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: 1 – Basics of LLMs

Topics

  • What is generative AI and how it's different
  • Understanding LLMs
  • Advantages and Common Industry Applications
  • Bonus section: Google Gemini and Multimodal LLMs

Module: 2 – Word Vectors

Topics

  • 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
  • Bonus Resource: Talk on Future of LLMs by the Co-Creator of ChatGPT, Łukasz Kaiser

Module: 3 – Prompt Engineering

Topics

  • Introduction and in-context learning
  • Best practices to follow: Few Shot Prompting and more
  • Token Limits
  • Prompt Engineering Exercise (Ungraded)

Refresher Module

Topics

  • Overview of learnings so far sent over registered email address.
  • Release of bootcamp keynote session(s).

Will be sent via email to registered email IDs: Register here

Module: 4 – RAG and LLM Architecture

Topics

  • 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)

Module: 5 – Hands-on Development of Realtime LLM ApplicationsQu

Topics

  • Installing Dependencies and Pre-requisites
  • Building a Dropbox RAG App using open-source
  • Building Realtime Discounted Products Fetcher for Amazon Users
  • Building RAG applications with local models
  • Leveraging Pathway with LlamaIndex/Langchain (Bonus)
  • Problem Statements for Projects
  • Project Submission

Module: 6 – Project Development: Tracks and Submission

Topics

  • Problem Statements Release for the Projects
  • Window for Sharing/Reviewing Project Ideas via Discord Channel
  • Online Office Hours
  • Projects Submission
  • Project Feedback (after the submissions deadline)

What are Bonus Sections/Resources?

Throughout the bootcamp, you'll see some modules or links labeled as bonus resources. These are not compulsory for building a project by the end of the bootcamp or attempting the quizzes.

Nonetheless, they are relevant resources that could enhance your understanding, although they might require additional prerequisites. Depending on your starting point and the pace you're progressing through the bootcamp, you can explore or park these bonus materials.