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Designing Practical Evaluation Loops for LLM Products

2026-02-01

A field guide for moving from one-off demos to reliable AI product behavior.

Teams usually fail with LLM products in one predictable way: they only evaluate outputs when a bug is already visible.

A practical evaluation loop is simpler than most teams assume:

  1. Define high-impact scenarios first.
  2. Turn those scenarios into repeatable test cases.
  3. Add weekly review on failures and drift.
  4. Fix prompt, retrieval, or orchestration before adding more features.

This sequence keeps progress stable and protects delivery speed.