Course Syllabus

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 best use generative AI tools such as ChatGPT.

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

What we'll be learning to get there:

1 – Basics of LLMs

• What is generative AI and how it's different

• Understanding LLMs

• Advantages and Common Industry Applications of LLMs

• Bonus section: Google Gemini and Multimodal LLMs

2 – Word Vectors

• What are word vectors and word-vector relationships

• Role of context in LLMs

• Transforming vectors in LLM responses

• Bonus Resource: Overview of Transformers Architecture and Vision Transformers

3 – Prompt Engineering

• Introduction and in-context learning

• Best practices to follow: Few Shot Prompting and more

• Token Limits

• Prompt Engineering Peer Reviewed Exercise

4 – RAG and LLM Architecture

• Introduction to RAG

• LLM Architecture Used by Enterprises

• Architecture Diagram and LLM Pipeline

• RAG vs Fine-Tuning and Prompt Engineering

• Key Benefits of RAG for Realtime Applications

• Simialrity Search for Efficient Information Retrieval

• Bonus Resource: Use of LSH + kNN and Incremental Indexing

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