LLM features without evals ship on vibes. Eval basics for product teams are simpler than research benchmarks: a golden set, a pass rule, and a regression gate before every prompt or model change.
1. Build a golden set first
Collect 20 to 50 real cases from your domain. Include easy wins, edge cases, and out-of-scope prompts. For RAG, store the expected source docs. For structured extraction, store the expected JSON.
2. Define pass criteria
- Schema validity (JSON parses and required keys exist)
- Citation presence when retrieval was required
- Policy compliance (no competitor pitches, no secrets)
- Human acceptance rate on a sample
- Exact match only where the task is deterministic
3. Regression workflow
Run the golden set in CI or a staging script when prompts, models, chunking, or tools change. Fail the deploy if pass rate drops below your threshold. Keep a short changelog of prompt versions.
4. What not to overbuild
Do not wait for a perfect LLM judge. Start with rules + spot checks. Add model-graded evals later for style or long answers. Never let automated scores replace a human review desk for high-risk outputs.
Summary
Evals turn AI from a demo into engineering. Golden set, pass rule, regression gate. That is the minimum bar.
Related: LLM Application Development, RAG Development, Hire AI Developer.
Prakash Tank
Full-Stack Architect & Tech Enthusiast. Passionate about building scalable applications and sharing knowledge with the community.
