Definition
A hallucination is a confident statement from an AI model that is not supported by any source, and often not true. Unlike a retrieval error, where the system pulls the wrong document, a hallucination is generated from the model’s own parameters. For a brand, the risk is an invented product, price or policy stated as fact.
At a glance
- CategoriesCore Concept
- Related fieldsAI Search
- DifficultyBeginner
At a glance
- A hallucination is fluent and confident, which is what makes it hard to catch.
- The cause is generation without support, not a database returning the wrong row.
- Hallucinations cluster where sources are thin, contradictory or out of date.
- A citation beside a sentence does not mean the cited page says what the sentence says.
Why a model states a wrong fact with a straight face
A language model predicts likely continuations. It holds no list of facts to check itself against. “Plausible” and “true” are different properties, and only one is being optimized.
Retrieval narrows the problem without solving it. Grounding is the discipline of holding the model to the passages it was given, and Google Cloud’s grounding overview frames the fix as connecting the model to verifiable sources.
The failure happens at the seams. A question needs five facts, retrieval supplies four, and the model still produces a whole answer. The missing fact gets generated in the same register as the rest, sometimes beside a citation belonging to the neighbouring sentence. Models are not noticeably less fluent when wrong.
When the invented fact is about your brand
The version that costs money is specific and mundane. An assistant tells a shopper your returns window is thirty days when it is fourteen. It quotes a price you retired last year. It gives your product a feature that belongs to a competitor.
The shopper does not check. They act on it and arrive at your support queue annoyed, or quietly discount you and buy elsewhere. Neither outcome shows up in a report as an AI problem.
The pattern is fixable. Hallucinations about a brand concentrate where public information is thin, stale or inconsistent. If your returns policy reads one way in your help pages, another in a marketplace listing and nowhere on your product pages, there is no clear signal to retrieve, so the model reaches for what returns policies usually look like. Source credibility signals decide between versions that differ.
Cut the odds a model invents your details
Fixing it takes no tool. It takes your facts to be findable and to agree with each other.
- Write your key facts in plain text on your own site. Returns window, shipping times, pricing tiers, what the warranty covers. One page, unambiguous sentences, no PDF.
- Audit the third-party versions. Marketplace listings, directory profiles, old press releases. Anywhere your facts appear wrong can be retrieved and quoted.
- Remove or update stale pages. An outdated price page you forgot about competes with your current one at retrieval time.
- Test the questions you would hate to get wrong. Ask each assistant about your returns, pricing and compatibility, and record what comes back.
- Re-check after you fix something. Correction is not instant, and knowing how long it takes for your site beats any general estimate.
Hallucination, retrieval error and stale data
| What happened | What fixes it | |
|---|---|---|
| Hallucination | The model generated an unsupported claim | Publish the missing fact clearly, in text |
| Retrieval error | The system found the wrong page | Fix on-site clarity and structure so the right page wins |
| Stale data | The source is real but out of date | Update or remove the old page |
| Conflict | Two sources disagree | Make your own sources consistent |
Frequently asked questions
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Can I report a hallucination about my company?
Most assistants take feedback on an individual response, and it is worth using, but it does not work like a takedown. No ticket, no confirmation, no timeline. The reliable lever is changing what the system retrieves: publish the correct fact clearly and get the wrong sources corrected.
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Is a hallucination the same as being out of date?
No, and the fix differs. Out of date means the model is reporting something true before its knowledge cutoff, or reading a page you never updated. A hallucination has no source at all. Check whether the claim was ever true: if it was, you have a stale-source problem, which is easier to solve.
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How often does this actually happen to brands?
Often enough to check, rarely enough to panic. Anyone quoting a precise rate is estimating, because it varies by category, by how much has been written about you, and by which assistant you ask. Run your own test on the questions that matter. That number is real and it is the one worth acting on.
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Will hallucinations be solved?
Reduced, not eliminated, and treating “solved” as imminent leads to bad decisions. Grounding, verification passes and better retrieval have all helped. The underlying behavior, generating plausible text where knowledge runs out, is part of how these systems work.