Building RAG that doesn't hallucinate

Retrieval-augmented generation is the workhorse of serious applied AI: give a model the right context at the right moment and it stops guessing. But RAG is only as trustworthy as its retrieval and its evaluation. A demo that dazzles can still hallucinate in production — here is how we keep it honest.

Retrieval is the whole game

If the right passage never reaches the model, no prompt will save the answer. We invest where it counts: thoughtful chunking, hybrid keyword-plus-semantic search, and reranking so the best evidence lands at the top of the context window.

Make it cite its sources

Every claim should trace back to a retrieved document. Inline citations aren't just UX — they are a forcing function that keeps the model grounded and makes a wrong answer visible instead of merely plausible.

• Ground answers in retrieved text, and say "I don't know" when evidence is thin.
• Show sources so a human can verify in one click.
• Guardrail the edges — refuse, fall back or escalate rather than invent.

If you don't measure it, it drifts

We ship RAG with an evaluation harness: a graded set of real questions, scored on faithfulness and relevance, run on every change. Quality becomes a number you can defend — not a vibe.