Module 4 · Vector search & RAG

Module 3 built a knowledge base; this module teaches your agent to actually use it. You will learn what retrieval-augmented generation is, how top-K controls how much context the model sees, how vector and keyword search differ, how hybrid search fuses them with RRF, and how to ground answers with citations the user can click through to the source text.

1What is retrieval-augmented generation?Retrieval-augmented generation (RAG) answers a question by first retrieving the most relevant chunks from your documents, augmenting the prompt with them,… 6 min

2Top-K retrievalOnce every chunk is ranked by similarity, you choose how many to send the model — that count is K. Too low and you miss the answer; too high and you bury… 7 min

3Vector, keyword, and hybrid searchThere are two ways to search: vector search matches by meaning (Module 3), and keyword search matches by exact words. They fail in opposite ways, so… 8 min

4Citations: closing the loopRetrieval gives the model the right facts; citations let the user verify them. The model marks each fact with [N] pointing at the chunk it used, and the… 6 min

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