Module 12: Generative AI, LLMs & RAG
Understand Transformers, fine-tuning, vector databases, LangChain, RAG, prompt engineering and an end-to-end document Q&A project.
How to use this module: follow the lessons in order if the stack is new to you, or jump directly to the topic that matches your project. Each lesson includes runnable examples, references and a downloadable notebook.
Transformers & Attention
Transformer Architecture & Self-Attention
LLM Fine-Tuning
Fine-Tune LLMs with Hugging Face
Vector Databases
Vector Databases: Chroma, Pinecone & FAISS
LangChain Basics
LangChain Tutorial for Python Beginners
RAG Architecture
RAG Tutorial: Architecture, Retrieval & Evaluation
Prompt Engineering
Prompt Engineering Techniques for Developers
RAG Project
Build a RAG Q&A App with LangChain
Practice strategy
Run the examples locally, change one assumption at a time, and record what changes in output, latency, memory or model quality. For production topics, add one failure test before you call the exercise complete.
Next step
Return to the full Data Science curriculum to continue with the next module.
