Feedback to Refinement Loop
Turn production signals into ranked prompt and tool changes, each tested before users ever see it.
Description
Turn what you learn in production into prompt and tool changes, on a loop. Feed traces, user signals, and outcome metrics into an automatic detector that flags problems, then a review queue where a person checks them. Confirmed problems become prompt and tool fixes. Test each fix before it ships. This is how you run a live LLM app every day, not a one-time cleanup project. It ranks the fixes by user pain, so the team works on what hurts users, not on hunches.
When to apply
Use this when an LLM app or agent is live with real users, you are capturing telemetry, and your job is to keep quality high over time, not just to launch. Don't apply it before launch. There is no production signal yet, so the loop becomes a hypothetical pipeline. One exception: a closed beta with representative users and live telemetry counts as production for this loop.
What it involves
- Build the feedback pipeline
- Automate issue detection and root-cause analysis
- Human-in-the-loop review
- Refine prompts and tools
- Aggregate and prioritize improvements
- Re-validate via experimentation
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