AI hallucinations: how to spot and reduce invented answers
An AI hallucination is an incorrect or context-inconsistent answer that can still sound convincing. You reduce risk by setting clear boundaries, providing evidence and checking claims that matter.
This workflow does not promise to eliminate mistakes. Its purpose is to recognize what needs review before treating an output as information or using it for a decision.
1. Look for unsupported precision
Exact numbers, study titles and quotations attributed to people deserve attention when no verifiable evidence accompanies them. That is a reason to check, not automatic proof of an error.
An address can exist without supporting the sentence. Open the reference and find the passage the answer claims to represent.
2. Compare the answer with supplied material
For summarization or extraction, check whether names, units and conditions remain intact. Information can be generally plausible and still be wrong for the particular document.
For example, changing “may happen” into “will happen” alters the degree of certainty. Review should detect these changes as well as completely invented facts.
3. Provide a way to express uncertainty
Anthropic’s hallucination-reduction documentation recommends allowing the system to acknowledge limits. In practice, specify what should happen when information is missing.
Use only the supplied material to answer.
Distinguish explicit information, interpretation and points not found.
When support is missing, write “this material does not confirm it”.
Do not create numbers, references or examples presented as facts.
List claims I should verify before using the answer.An instruction like this organizes the task, but it also needs testing. Do not treat a model-generated “verified” label as an independent audit.
4. Check what changes your next action
Start with deadlines, costs, requirements and other details affecting the next step. Prioritizing does not mean accepting everything else unread; it means allocating review time consciously.
| Question | Next action |
|---|---|
| Is the claim in the document? | Find the supporting passage |
| Does it depend on current information? | Consult a recent primary source |
| Was the source not found? | Mark the claim as unverified |
| Is it only a suggestion? | Label it as a proposal, not a fact |
5. Correct more than the wrong premise
When you find an error, supply the correct evidence and request a review of affected conclusions. Do not stop at replacing a name if the whole argument depends on it.
Also notice whether the answer merely agrees with your hypothesis. Ask questions that allow a different conclusion supported by the material.
6. Record what remains unknown
A useful answer can end with an explicit gap. That lets you research what is missing without carrying false certainty into the next stage.
Use the source-checking workflow and summary review when working with documents.
Frequently asked questions
Does a confident answer mean it is correct?
No. Fluency and confidence do not replace evidence or fact-checking.
Does telling AI not to invent solve the problem?
Not by itself. Combine instructions, reference material and review of important claims.
Is every disagreement between two answers a hallucination?
No. Different interpretations, outdated information or different criteria may be involved; identify the claim and check the evidence.