A retrieval-augmented chatbot is easy to demo and hard to trust. The gap usually isn’t the model — it’s everything around it: how documents are chunked, how retrieval is scored, and whether anyone is measuring answer quality at all.
The demo trap
Most proofs of concept are tested on a handful of clean documents and a few friendly questions. Production traffic looks nothing like that. Users paste in messy context, ask multi-part questions, and expect citations.
If you can’t measure retrieval quality, you can’t improve it — you’re just rerolling prompts and hoping.
What we check before shipping
- A labeled evaluation set drawn from real user questions
- Retrieval scored independently from generation
- Guardrails for “I don’t know” instead of confident hallucinations
- Monitoring on live answers, not just offline tests
None of this is exotic. It’s the difference between a demo and a system you can put in front of customers. Start with evaluation, wire in retrieval metrics, and the rest of the pipeline gets a lot easier to reason about.
Building something with retrieval or agents? We help teams ship AI features that survive real users.