Hallucination
Hallucination explained
For brands this is a reputation problem with a technical fix path. Hallucinations about a company usually happen where the public record is thin or contradictory. The model fills the gap with something plausible, and plausible is not the same as true.
The pattern we see with Indian D2C brands: wrong founding year, wrong founder name, discontinued products described as current, and confident statements about ingredients or certifications that were never claimed. Each of those traces back to a source the model could find, or to the absence of one.
The fix is boring and effective: publish the correct facts in structured form on your own site, get them repeated on two or three independent sources, and re-test the same prompts weekly until the answer changes.
Problems this term shows up in
Service that owns this work: Entity and Schema Engineering
One definition is worth more than ten opinions
Every term here links to the service that owns the work and the tool that measures it.
Read next
Three things you can test this week
None of these need a tool or a vendor. They take an afternoon and they tell you whether hallucination is actually your problem.
Our own measurement method, with its definitions and stated limits, is published on the method page.
The definition is the easy part
Knowing what a term means takes five minutes. Knowing whether it is your problem takes an afternoon, and knowing what to do about it takes a method.
Every glossary page here ends with three checks you can run yourself and the primary sources behind them, because a definition you cannot act on is trivia.
Primary sources
Primary documentation for the mechanics described on this page. All published by the organisations that operate the systems in question.
Twenty buying prompts, four engines, you against three named competitors. Report in one working day. No call needed.
Hallucination: common questions
Can you force a model to correct itself?
Not directly. You change what it can retrieve, and the answers follow, usually within weeks for retrieval based engines.
Do corrections stick?
Retrieval based answers correct fastest. Answers drawn from training memory lag until the next model refresh.
Is it worth chasing every wrong detail?
No. Chase the ones a buyer would act on: price, safety, ingredients, availability, and who you are.
See what the engines say about you
Twenty buying prompts, four engines, your brand against three named competitors. Report in one working day.
- 20 buying prompts, your category
- ChatGPT, Gemini, Perplexity, AI Overviews
- You against three named competitors
- Report in one working day, yours to keep