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A generated output can be useful without being factual, original in a legal sense, or appropriate for publication. Those qualities require separate checks.

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• Match evaluation to the generated artifact.
• Distinguish source facts from model additions.
• Keep a review and correction path.

Different generation systems use different mechanisms. An autoregressive text model predicts successive tokens. Diffusion-based image systems learn to transform noisy representations into samples. These are model families, not guarantees about every product or implementation. A prompt specifies a task and context, but a complete application may also retrieve documents, invoke tools, or filter outputs. Supplying source material can improve relevance while still leaving room for omissions and unsupported claims. Separate what a source states from what the model infers. Evaluate outputs according to their use. For summarization, check factual consistency and coverage. For code, inspect behavior and run meaningful tests. For images or audio, review artifacts, consent, and the intended use of recognizable people or protected material. One broad preference score cannot settle all of these questions. Use a workflow with a clear review point and a way to correct mistakes. Record the model version, prompt, relevant source material, and settings when reproducibility matters. A second generation may differ, so preserve the actual output used in a decision or published artifact.

1. Construct a meeting note with three decisions, two open questions, and one tentative suggestion.
2. Ask for a summary, then label each generated statement as supported, omitted, or added beyond the note.
3. Revise any tentative suggestion presented as a final decision and restore any missing owner or deadline.

This illustrative review method checks fidelity to a source instead of judging only the smoothness of the prose.