Practical Evaluations for AI Features

What you need to learn fast

You do not need a perfect benchmark to ship a reliable AI feature. You need a tight loop that tells you if users are getting value, where it breaks, and how safe it is to automate more of the work. The goal is to answer three questions quickly: does it help a real user finish a real task, can we trust it under normal load, and what should we improve next week. Keep the scope tight and the timeline short so the team builds momentum.

Define the core task

Write down the exact input and the exact output. Avoid vague goals like “better answers.” Use a schema or rubric that a reviewer can score in seconds. If you cannot score it quickly, you cannot improve it quickly. A simple example: given an inbound support email, return a JSON object with category, priority, suggested template, and confidence, plus a short justification. This forces clarity and makes both automated and human review straightforward.

Build a small, trusted eval set

Start with 25–50 real examples from production. Cover the common case, a few edge cases, and known traps. Store the inputs, the expected outputs, and short notes on why they are correct. This becomes your north star. Keep examples fresh by rotating in new cases every few weeks, and record who labeled them and why. Two reviewers agreeing on the rubric is often enough to ensure the set is trustworthy without slowing you down.

Test offline before you test online

Run your candidates against the eval set before exposing users to changes. Track exactness, partial credit, latency, and failure modes. Keep the numbers and the examples together so you can see patterns, not just scores. Always compare against a simple baseline so you know if complexity is earning its keep. Save artifacts from each run with a version tag so you can reproduce results and explain decisions.

Add guardrails to the test itself

Validate outputs against a schema. Require citations when facts are involved. Reject outputs that miss required fields or include unsafe actions. This prevents “looks good” results that fail in production. Prefer deterministic tools wherever possible and test them independently. When validation fails, capture the reason so you can guide retries or send the case to a human promptly.

Measure value, not just accuracy

Accuracy is useful. The goal is impact. Time saved, rework avoided, and successful completions tell you if the feature moves the needle. When value goes up and intervention goes down, you are ready to automate more. Attach one primary business metric to each feature so trade‑offs are explicit. If accuracy drops slightly but completion time improves a lot, you might still be winning.

Close the loop with production signals

Capture reason codes on overrides. Sample sessions every week. When a pattern emerges, add new cases to the eval set and adjust prompts, tools, or policies. Your test suite should grow with your product. A short weekly review with examples on screen often surfaces quick fixes that move the metric. Treat misses as input to better tooling and clearer contracts, not just prompt tweaks.

Bottom line

Small, realistic tests plus a weekly cadence beat large, abstract benchmarks. Make it easy to tell if the feature helps someone finish the job. Keep the suite small enough that it runs fast and is easy to maintain.

Implementation checklist

Metrics to watch

Common pitfalls to avoid