Building a RAG Q&A System End-to-End
RetrievalQA chain, custom prompts, source attribution, evaluating RAG with RAGAS.
What is RAG?
Retrieval-Augmented Generation grounds an LLM in your own data. At query time you retrieve relevant chunks from a vector store and inject them into the prompt, so the model answers from facts instead of guessing.
The RAG Flow
Every RAG query follows four steps: (1) embed the question, (2) retrieve similar chunks, (3) stuff them into the prompt as context, (4) the LLM generates an answer grounded in that context.
All lessons in this course
- LangChain Architecture and LCEL
- Document Loading, Splitting, and Embedding
- Vector Stores: Chroma and FAISS
- Building a RAG Q&A System End-to-End