Retrieval-Augmented Generation (RAG) is the most important technique for grounding LLM answers in your own data. Instead of hoping the LLM memorized the right facts during training, you retrieve relevant documents and stuff them into the prompt.
This chapter builds a complete RAG pipeline from scratch — no frameworks, no abstractions. You’ll implement every step manually: chunking, embedding, vector search, prompt construction, and generation. This gives you the deepest understanding of what frameworks like LlamaIndex and LangChain do under the hood.