Build a RAG Chatbot Over Your Docs
An agent that answers questions from your own documents instead of guessing. The retrieval quality matters more than the model.
- 1
Get retrieval right first
Before adding any agent logic, build a plain retrieval pipeline: chunk your documents sensibly, embed them, store them in a vector index, and test that a question returns the right passages. LlamaIndex is built for this. Most RAG quality problems are retrieval problems, so prove retrieval works before layering an agent on top.
- 2
Let the agent decide when to retrieve
A naive RAG bot retrieves on every message. An agent can be smarter: it decides whether a question needs a lookup, what to search for, and whether one retrieval is enough. Wrap retrieval as a tool the agent can call, so it reasons about what it needs instead of stuffing context blindly.
- 3
Ground answers and cite sources
Tell the model to answer only from the retrieved passages and to say when it does not know, which cuts hallucination. Return the source chunks alongside the answer so users can verify. For a TypeScript stack, Mastra covers retrieval and the agent loop in one package; for Python, LlamaIndex plus an orchestrator works well.