AI Hallucinations: Diagnose and Correct Unsupported Claims

Recognise the error without relying on how it sounds

A hallucinated statement can look ordinary. It may be a plausible publication date, an invented menu option or a quotation attributed to someone who never said it. Dramatic nonsense is easier to notice, but subtle errors can be more consequential because they fit the surrounding explanation. A model's polished wording is not an evidence signal. Read important claims as assertions that need support, especially when the answer names a specific source, gives a precise number or tells the reader to perform an action. The more consequential the detail, the less appropriate it is to accept it on fluency alone.

NIST's Generative AI Profile includes confabulation among generative AI risks. Terminology varies, and not every wrong answer has the same cause. A model may invent a detail, misunderstand a supplied passage or accurately repeat an incorrect source. For diagnosis, describe the observable failure before assigning a broad label. “The answer names a settings control absent from the current documentation” is more useful than “The AI is broken”. It establishes what needs checking while leaving room to discover whether the problem comes from outdated documentation, version mismatch or unsupported generation.

Inspect named sources and quotations first

A citation can be fabricated completely or combine real elements incorrectly. A real author may be attached to a nonexistent paper, or a genuine paper may receive an invented finding. Search for the original work through its publisher, official repository or author institution. Open the actual document and locate the claimed passage. Do not use another generated summary as the sole verification source. The citation quality guide explains claim-level support checks. A link resolving successfully proves only that a resource exists; it does not establish that the resource says what the answer attributes to it.

Quotations deserve exact checking because paraphrase and quotation make different promises. If the wording cannot be found in the original, remove quotation marks and verify whether a restrained paraphrase is supported. Do not “repair” an invented quotation by leaving the same idea attached to the speaker without evidence. Numerical claims need similar scrutiny: identify the population, period, unit and measurement method. An illustrative number used in an explanation should remain labelled illustrative. A model can make an invented example sound like a measured case study simply by adding a customer name and an apparently precise percentage.

Separate unsupported additions from source mistakes

Compare the answer with the material actually supplied or linked. If the source says a trial lasts fourteen days and the answer says thirty, the response has changed a relevant fact. If the source itself says thirty but the current policy is fourteen, source maintenance is implicated. Grounding explains why these are different checks. In an owned application, preserve the retrieved context so the distinction can be tested. In a public answer engine, acknowledge that visible links may not reveal every internal input. You can still identify an unsupported published assertion without claiming access to the hidden cause.

An illustrative support answer might describe a “restore account” button that does not exist. Check the product version and user role before concluding that the feature was invented. If the control existed in a retired version, the answer may be stale or mismatched rather than wholly fabricated. The remedy still requires correction, but the diagnosis affects where to act. Update obsolete source pages, clarify version labels or improve retrieval eligibility where you control them. If the source is accurate and current, focus on the generated claim and the application's handling of uncertainty instead of rewriting correct documentation unnecessarily.

Capture a failure so someone else can examine it

Preserve the exact prompt, answer, date, platform or application and any relevant settings visible to you. Include the conversation context when it changes the meaning of the prompt, but remove personal or confidential information that the recipient does not need. Save the cited URLs and the passages used to verify the error. Record whether the result occurred once or across repeated tests. A screenshot can show presentation, while copied text makes the claim searchable. The prompt tracking guide provides a reproducible baseline for repeated observations without pretending that one sample represents every possible response.

State the expected correction in bounded terms. “Replace the unsupported thirty-day claim with the documented fourteen-day trial period for this product version” is actionable. “Make the model never hallucinate again” is not a realistic completion condition for one issue. Classify the consequence: a cosmetic misdescription, an unusable instruction, a false attribution or a decision-changing error may deserve different urgency. Avoid exaggerating the scope to win attention. Precise reports are stronger because reviewers can reproduce the discrepancy and understand why it matters, rather than spending the first discussion disentangling a general complaint from the actual evidence.

Correct the information you control

If your own page contains an incorrect claim, repair it and all connected representations. The summary, table, diagram and metadata may repeat the same error. Add a correction note when the change materially affects what readers may already have relied upon. Route the work through content governance so ownership and future review triggers are clear. Editing the page does not directly rewrite every model or cached answer. Distinguish the completed source correction from later observations of external answers, and avoid claiming immediate universal removal of the old statement without evidence.

For an owned assistant, test the corrected source and response path. Check whether the new material was ingested, whether retrieval selects it and whether generation preserves its conditions. RAG offers a way to supply maintained evidence, but adding retrieval does not automatically eliminate errors. The original RAG research studies a particular retrieval-and-generation approach; it is not a guarantee that every implementation will answer accurately. Use a targeted regression question for the known failure, then include nearby cases to check that the repair did not merely memorise one wording while leaving the underlying confusion intact.

Reduce opportunities for invention during drafting

When using AI to write, provide the intended scope, relevant evidence and a clear instruction to flag missing support. Ask for illustrative examples separately from factual claims. Keep unverified suggestions out of the finished prose until they have been checked. The AI-assisted writing guide covers the full editorial process. A second model can help spot suspicious claims, but agreement between models is not independent evidence. Both may repeat a common misconception or produce the same plausible completion. Original sources and direct verification remain necessary for consequential statements.

Allow uncertainty to produce a useful next step. An answer can explain the supported information, identify what is unknown and suggest where the reader can confirm the missing detail. This is preferable to filling a gap with an authoritative-sounding guess. It is also more useful than replacing every statement with vague caution. The goal is calibrated specificity: clear where evidence is strong and explicit where it ends. Treat hallucination handling as a process of verification, diagnosis and bounded correction. That approach turns an alarming error into a concrete information problem while preserving an honest account of what has and has not been fixed.

Sources and further reading

• NIST: Generative AI ProfileNIST published a generative AI companion profile addressing risks including confabulation.
• Lewis et al.: retrieval-augmented generationThe paper combines a retriever over external documents with a sequence-to-sequence generator.