A useful AI project starts with the workflow
Before choosing a model, understand the task, the person doing it, and the decision that follows.
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Real examples and clear acceptance criteria turn an impressive demo into a system you can assess.
Begin with representative examples
Collect examples from the work the system is intended to support. Include routine requests, difficult inputs, and situations where the correct response is to ask a question or stop. Protect sensitive information and keep a portion of the examples separate from development so that evaluation remains informative.
Use criteria tied to the task
There is no single score for a good AI system. A document extractor needs field-level accuracy and evidence. A support assistant needs relevant answers, sensible escalation, and respect for access rules. Define criteria in language the workflow owner can use, then choose measurements that reflect those criteria.
Inspect errors by consequence
An incorrect punctuation choice and an incorrect account detail should not receive the same attention. Classify failures by what they would cause in the real workflow. Reviewing severe errors separately helps teams avoid hiding an important weakness behind a favorable average.
Keep evaluating after release
Documents change, users ask new questions, and model behavior can shift after an update. Maintain a versioned evaluation set and run it when changing models, prompts, retrieval, or integrations. Pair those checks with production monitoring and a clear owner for investigating new failure patterns.
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