If ChatGPT or Perplexity has described your product with a feature you do not have, or missed one you do, you are seeing an Expertise problem. Here are the questions brands ask most about AI accuracy, with direct answers. For the full picture, see the Expertise pillar guide.
Why does AI list the wrong features for my product?
Because it synthesizes its answer from the sources it can read, and those sources are incomplete, outdated, or unclear. If your own documentation is thin or written in marketing language, the engine has little accurate material to draw on, so it fills the gap with a plausible guess that is often wrong. Old reviews and stale third-party pages also persist and get repeated. The engine is not lying. It is reflecting and guessing from imperfect sources.
Is AI making up facts about my product?
Sometimes, yes. When a model lacks a clear source for a specific capability, it can produce a confident, specific, wrong statement, which is what people mean by AI hallucination. It is most likely to happen where your information is sparse or ambiguous, because the model is built to give a fluent answer rather than admit a gap. The fix is to remove the ambiguity by publishing clear, current, factual capability content.
Why does AI describe an old version of my product?
Because the sources it read describe the old version. Engines reflect what is published, so retired pricing, removed limits, and replaced features keep appearing until the underlying pages, docs, and listings are updated. Treat your capability information as something to maintain after every release, not publish once.
Why is AI confusing my product with a competitor?
Brands with similar names, or in the same narrow category, can get blended together when the sources do not draw a clear distinction. The engine attributes one brand's integrations or gaps to the other. Clear, consistent, well-structured descriptions of your own product, and your own name used consistently across the web, reduce this kind of mistaken identity.
How is fixing this different from SEO?
Traditional SEO is about ranking a page in a list of links. Keeping AI accurate about your product is answer engine optimization (AEO), sometimes called generative engine optimization (GEO): shaping the sources an engine synthesizes so the facts it repeats are correct. You can rank well in search and still have AI state the wrong specs, because the engine is drawing on more than your ranked pages.
Does it matter if AI gets one feature wrong?
Yes, more than it looks. A single wrong fact can disqualify a customer who had a hard requirement, and they leave without telling you. In a HiBot audit, a material factual error also caps that answer's score at 2 regardless of how positively you are framed, and it pulls down your Accuracy Rate. A confident wrong answer is treated as a loss, because to the customer it is one.
How do I check what AI says about my product's capabilities?
Run specific prompts about your real features, integrations, and specs, logged out, across more than one engine, and record every inaccuracy. You can structure the check with The Capability Accuracy Audit. Keep in mind that answers vary from ask to ask, so a single check is a hint rather than a full picture.
How do I get AI to describe my product correctly?
Publish plain, factual, current capability content, structure it so the right fact is easy to extract, keep specs and integrations updated everywhere an engine reads them, and earn citations to your own documentation. The step-by-step version is in How to Improve Your Expertise Score.
If you want to know exactly what AI gets wrong first, a HiBot audit measures your Expertise and Accuracy Rate across engines and ranks what to fix. Download the ANSWER whitepaper to see the method, or request an AI visibility audit at hibot.com.
