Before everything i want to tell you that i am working on a RAG project and you can check
https://www.ailog.fr and our app
https://app.ailog.fr/ .
You can check it out if you want a production-ready RAG ( we have an API and we can scale to enterprise level if necessary ).
Next for the feedback part :
Evaluate LLM systems as three separate layers: model, retrieval or grounding, and tools. Measure each with automated tests plus continuous human sampling. A single accuracy metric hides user frustration. Instrument failures, not just averages.
Practical framework you can implement quickly:
Human in the loop: Review 1 to 5 percent of production sessions for correctness, safety, and helpfulness. Train a lightweight risk flagger.
Synthetic tests: 100 to 500 canned conversations covering happy paths, edge cases, adversarial prompts, and multimodal failures. Run on every change.
Retrieval and hallucinations: Track precision at k, MRR, and grounding coverage. Use entailment checks against retrieved documents.
Tools and integrations: Validate schemas, assert idempotency, run end to end failure simulations. Track tool call and rollback rates.
Telemetry and drift: Log embeddings, latency, feedback, and escalations. Alert on drift, hallucination spikes, and tool failures.
Weekly metrics: correctness, hallucination rate, retrieval precision at 5 and MRR, tool success rate, CSAT, latency, escalation rate.
Pilot plan: one week to wire logging, two weeks to build a 100 scenario suite, then nightly synthetic tests and daily human review.
You can check out https://app.ailog.fr/en/tools to get some insight on way to evaluate your RAG, we have free tools here for you to check and use :)
