Why AI can invent convincing details

A fluent answer can contain a nonexistent API option, fabricated citation, or incorrect explanation. Generated text is not automatically checked against an authoritative record.

A product can retrieve sources and run tools, which gives it additional evidence. The final interpretation can still be wrong. Check the consequential claim and the evidence that actually supports it.

Understand the mechanism and its limits

Language models learn patterns that help them generate plausible continuations. Those patterns can produce accurate answers, but plausibility and factual support are different criteria.

Research by Kalai and colleagues examines how accuracy-based evaluation can reward guessing over admitting uncertainty. Under a scoring rule where an incorrect answer and abstention receive the same score, a guess has a chance to earn credit. That argument does not mean every model, task, or evaluation uses the same incentives.

Treat confidence in wording as insufficient evidence of correctness. Calibrated uncertainty requires evaluation; it cannot be established by a confident tone or a request to “be honest.”

Make uncertainty visible

For a technical decision, ask:

Separate documented behavior, observed behavior from the supplied tests, and inference. Link each consequential documentation claim to the relevant source and version. Mark unresolved details as unknown.

This format makes the result easier to inspect. It does not guarantee that the classification or sources are correct.

For structured extraction, permit an explicit unknown value instead of forcing every field to be filled. For code, ask for a reproduction and actual check results rather than a reassurance.

Trace one claim all the way

Suppose an answer says a CLI flag prevents every network request. Open the official documentation for the installed version. Check which operations the flag controls and whether custom tools or managed configuration change it.

Then verify the behavior in a synthetic environment where an unexpected request can be observed without exposing private material. Preserve the result and any remaining uncertainty.

A real citation can support a narrower claim than the sentence attached to it. Finding the page is only the first part of verification.

Use another model as a reviewer

An independent model can propose counterexamples or identify missing evidence. Give it the original material and criteria. Its agreement is another generated judgment, so resolve important disagreements with sources, executable checks, or an informed human decision.

The cross-provider review workflow shows how to keep that evidence available.

What goes wrong

Asking “are you sure?” can produce a more confident version of the same error. A search result can lead to a real page that does not support the claim. A code example can compile while violating the requirement. A schema-valid answer can still invent a source.

How to check

Take one important claim from a recent answer. Find its primary source or construct the behavior check that could disprove it. Verify the version and scope, then record whether the claim is supported, contradicted, or unresolved.

For arithmetic, recompute it. For a code change, test the affected behavior. For an interpretation you cannot evaluate, involve someone who can.