AI hallucination

Why do AI models hallucinate?#

AI models hallucinate because a large language model generates the most plausible next words rather than looking facts up, and training and evaluation reward a confident guess over admitting uncertainty — so rare facts get filled in with fluent fiction.

OpenAI researchers made the case in a September 2025 paper, Why Language Models Hallucinate: “Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty.” Benchmarks that score only right or wrong answers teach models that a guess beats “I don’t know.”

The paper’s most useful point for brands is about rare facts. Facts with no learnable pattern — a birthday, a founding date, a price — can only be memorized, and the authors estimate that “if 20% of birthday facts appear exactly once in the pretraining data, then one expects base models to hallucinate on at least 20% of birthday facts.” A fact about your company that appears on one page of the web is a fact a model is likely to get wrong.

Can AI hallucinate about my company?#

AI can hallucinate about any company, and the most common brand hallucinations are outdated prices, invented or missing features, wrong integrations, confusion with similarly named companies and fabricated citations — usually when the model answers from memory or from a thin, ambiguous source.

Fabricated sources are not rare: the Tow Center study found that more than half of Gemini’s and Grok 3’s responses cited fabricated or broken URLs. And because Gemini names brands in text far more often than it links them — only 21.4% of its brand appearances carried a link in a June 2026 Semrush study — an unlinked claim about you may have no source you can trace.

How do you measure AI hallucinations about your brand?#

Measure brand hallucinations with a fixed panel of prompts about your company, run in each engine on a schedule, with every factual claim checked against a single source of truth — then track the share of brand mentions that contain at least one false claim.

1. Write your fact sheet first: pricing, plans, core features, integrations, founding date, locations and who the product is for. This is the answer key.
2. Build 20–30 prompts buyers really ask: “How much does Plannora cost?”, “Does Plannora integrate with Slack?”, “Plannora vs Loopcraft for a small team.”
3. Run them in each engine, with search on and off where the product lets you. The difference shows whether an error comes from training or from a retrieved page.
4. Score each answer against the fact sheet, and log the cited source behind every wrong claim.
5. Repeat monthly and after every price or product change. Answers vary from run to run, so trends across several runs matter more than any single error. This is prompt tracking with an accuracy column.

How do you fix AI hallucinations about your brand?#

Fix brand hallucinations by publishing the correct facts where retrieval will find them — clearly titled, dated pages on your own domain, stated in plain sentences — and by correcting the third-party pages engines cite, since nobody can edit a model’s memory directly.

• Publish an official page for every checkable fact: pricing, plans, integrations, security, comparisons. Since August 2026 most ChatGPT fan-out searches use site: to check specific domains, often the vendor’s own, so an official page is frequently the first place it looks.
• State facts in citable sentences. “Plannora’s Team plan costs $8 per user per month, billed annually” beats a pricing grid rendered by JavaScript that no AI fetcher except Google’s can read.
• Date and update honestly. Show a visible updated date and change it only with real changes. See content freshness.
• Fix the sources engines cite. Ask review sites, directories and resellers to update stale listings, and require canonicals on syndicated copies of your content.
• Make the entity unambiguous. Consistent naming, a clear About page, and Organization schema with sameAs links help engines tell you apart from similarly named companies.
• Flag wrong answers with each engine’s feedback buttons. Vendors don’t say how that feedback is used, so treat it as a supplement, not a fix.

Related terms#

• How LLMs answerGroundingThe process of tying an AI model’s answer to specific, verifiable sources — usually web pages retrieved at the moment of the question — so the claims in the response can be checked and cited, rather than generated from the model’s memory alone.Read the entry
• How LLMs answerKnowledge cutoffThe date after which a large language model has no information from its training data, so anything newer — a launch, a price change, a new competitor — reaches its answers only if the model retrieves it from the live web.Read the entry
• How LLMs answerLLM training dataThe body of text — web crawls, books, code, licensed and forum content — that a language model learns from before release, and it shapes what the model says about a brand without searching, in a way that can’t be edited until the next model is trained.Read the entry
• LLMHow LLMs answerLarge language modelA neural network trained on vast amounts of text to predict the next word, which lets it write fluent answers — the technology behind ChatGPT, Claude and Gemini, whose knowledge is fixed at training time unless it retrieves fresh sources.Read the entry
• Authority & off-siteEntity SEOThe practice of making search engines and AI models recognize a brand, person or product as a distinct, well-described thing — an entity with consistent facts and relationships — rather than a string of keywords, so it can be tied to the topics it should be recommended for.Read the entry
• MeasurementPrompt trackingThe practice of running a fixed set of buyer questions through AI assistants such as ChatGPT, Perplexity and Gemini on a schedule and recording which brands and pages each answer mentions or cites — the AI-search equivalent of rank tracking.Read the entry