An AI response can sound convincing while missing information or introducing an error. Before using generated outputs in an ongoing workflow, define how you will judge them. Evaluation should reflect the actual task, the people affected, and the consequences of a mistake. A generic rating of whether an answer “looks good” does not provide enough evidence.
Write observable quality criteria
Describe what the output must contain, what it must avoid, and what a reviewer needs to verify. For extracting information from documents, criteria might include correct fields, preserved units, and clear treatment of missing values. For a draft summary, check whether important points are covered without adding unsupported statements. Separate factual correctness from style so a polished answer does not hide a substantive failure.
Use cases that reflect real variation
Include the typical input, difficult formatting, incomplete material, contradictory information, and requests outside the intended scope. Record why each example belongs in the set. Avoid measuring only easy cases that resemble a successful demonstration. Keep examples relevant to the actual audience and revisit the set when the workflow changes.
Define the reviewer’s job
Specify whether the reviewer checks every detail, samples lower-impact outputs, or approves a draft before it can be used. Consider the time and information required to perform that check. If the source is difficult to inspect, a nominal review step may not be effective. Provide a clear route for correcting an output and reporting a recurring failure to the person responsible for the system.
Compare usefulness with the existing process
Measure the full task rather than generation speed alone. Count preparation, review, correction, and exception handling. Consider response time and operating cost under representative conditions. A pilot may improve one part of a process while making another more difficult, so include feedback from the people receiving the final work.
- Document the input, output, and expected result.
- Score factual accuracy separately from presentation.
- Record missing, unsupported, and incorrect information.
- Track review effort and recurring failure types.
- Test again after a material system change.
Set a decision rule for deployment
Agree which failures block release and what evidence is needed before widening access or adding automated actions. Keep a record of the system configuration used for the evaluation so comparisons have meaning. Continue monitoring after introduction because inputs and user behaviour can change. Evaluation is a repeatable practice, not a certificate that makes future outputs automatically trustworthy.
Define success before reviewing the outputs, include difficult examples, and count the human work required. Use the evidence to choose a suitable scope and level of supervision.
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