Your AI feature looks good in the demo. The prompt works on the examples everyone has seen, the output sounds convincing, and engineering can ship. But you still cannot answer the release question that matters: how often will it fail across customers, edge cases, and the next prompt change?
You need more than a collection of impressive outputs. You need a repeatable workflow that turns “good” into named failure modes, measures those failures against representative cases, and shows whether a proposed change improves the product without quietly breaking something else.
Start with the release decision, not a generic quality score
An AI evaluation should help you make a product decision. Are you deciding whether to launch, whether a prompt is better, whether to change models, or whether a failure reported by a customer is now fixed? If the decision is unclear, the evaluation will usually collapse into a broad question such as “Is this output good?”
That question is too vague for a reliable test. LLM behavior is probabilistic, while the tasks assigned to LLMs are often semantic. The same input can produce different responses, and multiple responses can be acceptable without being equally useful. You therefore need to measure performance as a rate across cases, not certify the product from one successful run.
Begin with a one-sentence task boundary: “Given this input, the system should produce this kind of output for this user and purpose.” Then replace the word “quality” with observable errors. For an interview-synthesis workflow, those errors might include:
- A quoted sentence does not appear word for word in the transcript.
- A factual claim is not supported by the transcript.
- The output assigns the wrong status label.
- The response does not include the requested number of quotes.
- A fact about one person is incorrectly attributed to another.
Each definition tells you what to inspect and what a failure means. “The summary lacks quality” does neither. A precise definition also exposes disagreements early. If product, engineering, and a domain reviewer cannot agree on whether an unsupported inference counts as an error, a model score will not settle the product question for them.
Separate hard constraints from preferences. Grounding, correct classification, and required output structure can often be judged as pass or fail. Clarity, tone, concision, and usefulness usually need a rubric or a comparison between outputs. Combining both kinds of criteria into a single score makes a serious factual error look as if it can be offset by polished writing.
For every failure mode, record four things before running an experiment:
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