Production-Grade Evaluation for LLM Features
A lightweight evaluation stack for teams shipping LLM products.
LLM features require more than accuracy metrics. You must evaluate safety, relevance, and usefulness across user contexts.Start with a test suite of representative prompts, then add counterexamples that reflect edge cases. Build a baseline and keep it versioned over time.
Use multiple signals: automated metrics for quick checks, human review for nuance, and regression tests for stability after changes.
Include grounding checks when you use retrieval. If the answer cannot be traced to the context, the system should either abstain or ask for clarification.
Evaluation should run in CI or as part of release gating. It is cheaper to catch issues before users do.
The best evaluation stacks are small, repeatable, and tied to product outcomes instead of just model scores.
Key takeaways
- Evaluation must include safety and usefulness.
- Keep baselines versioned for regression testing.
- Combine automated and human review.
- Run evaluation as part of release gating.
Checklist
- Representative prompt suite defined
- Baseline results stored and versioned
- Human review process established
- Grounding checks for RAG outputs
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