Hallucination
When a model states something false or invented as though it were fact, with no signal that it is uncertain.
In depth
What it really means
Hallucination is a side effect of how these systems work. A language model predicts plausible next tokens. Plausibility and truth usually travel together, and when they part company the model has no internal alarm. The confident tone stays exactly the same, which is what makes it dangerous.
For brands the risk is specific: invented pricing, features you do not have, integrations you never built, a security certification you never earned. Buyers act on these. The defense is not arguing with the model, it is removing the information gap the model filled in for itself.
How it works
- The model reaches a point where it has no reliable stored fact.
- It generates the most statistically plausible continuation anyway.
- That continuation is fluent and confidently worded, because fluency is what it optimizes for.
- With no grounding step, nothing checks the claim before it reaches the user.
Pros & cons
Pros
- Recognizing hallucination patterns tells you exactly where your public information is thin.
- Hallucinated details about competitors reveal what buyers wrongly assume about the category.
- Fixing the gap usually improves the page for human readers too.
Cons
- You cannot force a correction, only change what the model retrieves next time.
- Wrong claims baked into training data persist until the next training run.
- Buyers rarely verify, so a hallucinated limitation can lose a deal silently.
Common mistakes
- Only checking the answer to ‘what is [brand]’ and never the questions buyers actually ask.
- Leaving pricing off the site entirely, which guarantees the model invents a number.
- Publishing capability claims without specifics, which leaves room for invention in both directions.
- Assuming a correction on your own site fixes it, when the model is reading a third-party page.
Best practices
- Publish exact, dated facts on the things models get wrong: pricing, limits, integrations, certifications.
- Prompt each assistant monthly with your real buyer questions and log factual errors.
- Trace each error to its source and fix it there, including third-party listings.
- State what you do not do as clearly as what you do, which removes ambiguity in both directions.
- Keep a changelog page so superseded facts are visibly dated rather than silently replaced.
FAQs
What is an AI hallucination?
When a model states something false or invented as if it were fact. It happens because the model predicts plausible text rather than verified truth, and it sounds identical to a correct answer.
Why do models hallucinate about my product?
Usually because the information is missing or ambiguous in public sources. If your pricing is not published anywhere, the model fills the gap with something plausible.
Can I get a hallucination corrected?
Not directly. You change what the model finds. Publish the correct fact clearly and get it onto the third-party sources the model retrieves from. Training-data errors persist until the next training run.
How do I check what AI says about my brand?
Prompt each major assistant monthly with the questions your buyers actually ask, not just your brand name, and log every factual error along with the source that likely caused it.
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