Quality has more than one dimension
HELM evaluates language models using multiple scenarios and metrics rather than a single accuracy score. For product builders, the useful principle is to define several dimensions of success. Correctness, robustness, efficiency, and the consequences of failure should not be collapsed into how convincing an answer sounds.
Start from the user task. An assistant that drafts a support reply needs different checks from one that extracts structured product data. Write acceptance criteria before collecting outputs so the team does not redefine success around whichever result the model happens to produce.
Technical foundation: Liang et al.: Holistic Evaluation of Language Models
Build a compact but representative set
Include normal requests, ambiguous inputs, missing evidence, unsupported tasks, and important failure cases. Use anonymized or synthetic examples when possible. Label synthetic cases honestly and verify that they resemble real usage; an easy artificial dataset can create misleading confidence.
For a documentation assistant, each case can record the question, allowed sources, required facts, prohibited claims, and acceptable no-answer behavior. Keep the set versioned. When a production defect appears, add a focused regression case instead of repeatedly expanding every test into a large, expensive workflow.
Use the cheapest reliable judge
Deterministic checks are useful for JSON validity, required fields, allowed references, and permission boundaries. Human review is important for usefulness, ambiguity, and nuanced factual support. A model-based judge can help triage some outputs, but its agreement with human reviewers should be checked.
Do not ask the same generator to certify its own answer without independent evidence. If a judge rates a response, give it explicit criteria and record the reason for the rating. Disagreements between judges are diagnostic information, not a reason to hide the result behind a single average.
Set a release decision and stop condition
Before running an experiment, define what evidence will change the decision. A prompt edit might need a targeted regression suite, not a complete benchmark rerun. A new tool with write access deserves additional authorization and recovery tests because its risk is different.
Report failures by category and retain the exact configuration: model identifier, prompt revision, tool version, and retrieval snapshot. Some model outputs vary between runs, so use repeated samples where variation matters. Avoid claiming perfect reliability from a finite test set, even if every recorded case passes.
An evaluation loop that stays useful
- Tie each case to a user outcome or known risk.
- Separate tuning examples from held-out checks.
- Use deterministic validation before expensive judging.
- Record configuration and classify failures.
- Stop when the agreed decision evidence is available.
