Model hallucinations: why plausible answers can be wrong

Why fluency misleads

Language models generate text based on learned patterns and current context. They can produce a convincing name, citation or explanation without a reliable basis for that particular claim. The problem is especially easy to miss when the output uses familiar professional language.

Make the evidence visible

For source-based work, ask the assistant to identify the passage behind each important conclusion. Open the sources yourself when the claim matters. A citation marker alone is insufficient: the linked page may exist without supporting the sentence. Separate direct evidence from interpretation and mark missing information explicitly.

Test the uncomfortable questions

Include questions with no answer in the supplied material, ambiguous dates and conflicting documents. Reward a clear statement of uncertainty when it is appropriate. For example, a policy assistant should explain that it lacks the relevant regional policy rather than adapting an unrelated one. Improvements may involve better retrieval, clearer instructions or a different workflow. No single wording trick removes the need to verify consequential claims against dependable evidence.

Sources & notes

AI-assisted editorial content checked against the linked sources.

OpenAI: Optimizing model accuracy

Sources reviewed for the September 2026 launch edition.