Why a Prompt That Looks Good Still Needs Systematic Evaluation

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A prompt's output can seem confident and correct while still containing errors, so a convincing result is not evidence that it works. The author argues teams should verify AI output against a defined 'Definition of Done' before trusting it, since a 100% acceptance rate may signal that nobody is checking. Prompts should be tested like code, using several real inputs rather than a single example, and re-tested whenever they run in production. Quantitative checks that a computer can run, such as word count or passing tests, should be automated first because they are cheap and fast. Tools like promptfoo can provide a lightweight evaluation framework to run these checks whenever a prompt changes.

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