Fluency is not proof
Models are good at making a partial brief sound complete. They can also inherit a mistaken premise from the prompt and repeat it with more confidence.
Before trusting a recommendation, separate what you supplied, what is externally documented, and what the model inferred. A strong tone belongs in none of those evidence categories by itself.
Use an adversarial second pass
Paste the recommendation back and ask for five serious objections, the assumption behind each objection, and the single fact that would resolve it. Ask which objection would stop the plan entirely.
The goal is not to force a negative answer. It is to reveal hidden dependencies such as customer demand, cost, permission, data quality, or a missing alternative.
Cross-check instead of looping
Repeatedly asking the same model often produces a more polished version of the same premise. Use a second model, an original source, a real user interview, or a small sample to create independent evidence.
Keep the prompt and scoring sheet stable so the comparison is meaningful. Record errors, citations, latency, manual edits, and the reason you accepted or rejected the output.
A disciplined APIToken test
Within the models lawfully available on APIToken, isolate a project key and submit the same redacted task. Inspect public status before testing and set a budget and retry limit.
A visible model is not a production guarantee. Keep the human reviewer in the loop, and stop when evidence is missing rather than paying for another confident paragraph.