Why do LLMs make things up about your brand?
The most solid explanation is offered by the paper Why Language Models Hallucinate by Kalai, Nachum, Vempala and Zhang (OpenAI/Georgia Tech, September 2025): language models work like students facing an exam that penalizes leaving answers blank. When they don't know, they guess. And current evaluation systems —the benchmarks that determine which model is "better"— reward that conjecture over honesty.
This doesn't seem like it's going to be resolved soon. A mathematical demonstration included in the same paper and reinforced by subsequent theoretical works (Kalavasis et al., 2025; Kleinberg and Mullainathan, 2024) suggests that, under current LLM architectures, there is a structural trade-off: any model that generalizes beyond its training data will inevitably produce invalid outputs or suffer mode collapse. That is: the model generates the statistically most probable answer, not necessarily the true one.
An MIT study (January 2025) adds an especially relevant nuance for whoever manages a brand: when a model hallucinates, it tends to use more assertive language than when it gets it right. Models were 34% more prone to use high-confidence expressions when generating incorrect information. The more the model is wrong, the more sure it sounds. And that is exactly what makes a hallucination about your brand dangerous: it doesn't arrive wrapped in doubts, it arrives with the conviction of a fact.
If your AI visibility strategy assumes models "know" what you are, you have a problem. Your brand needs a digital footprint robust enough that the model doesn't have to guess. That is part of what the Reputation layer addresses within the CREF© framework.
Brand de-biasing: what it is and what it isn't
In academic context, de-biasing refers to a set of techniques to reduce biases and hallucinations in language models. A review published in Artificial Intelligence Review (Springer, 2024) categorizes these techniques into data-based methods, fine-tuning, RLHF and generation control.
But when we talk about de-biasing applied to brand —what we call here "hallucination cleanup"— we refer to something different: monitoring what LLMs say about your company, detecting factual errors (hallucinations) and biases (outdated information, incorrect attributions, mis-compared competitors), and correcting them through a coordinated strategy of content, structured data and presence in sources that models crawl.
You can't retrain GPT-5. But you can influence what the model reads, cites and prioritizes when responding about your sector. If you control the sources that feed the model, you reduce the probability that it makes things up. It's not a guarantee —models still have their own dynamics—, but it's the most direct lever you have. To understand the distinctions between factual hallucination, bias and other related concepts, the Elevam GEO Glossary develops them in detail.
Brand de-biasing isn't retraining a model. It's building a digital footprint so clear, structured and verifiable that the LLM doesn't need to invent when a user asks about you.
How much do models hallucinate? Sizing the risk
Figures depend on model, task and benchmark. But there are patterns that help size what we're talking about.
The Vectara (HHEM 2.3, December 2025) leaderboard measures how often an LLM introduces false information when summarizing a document that has been explicitly provided. In that controlled task, the best models stand around 0.7% (Gemini 2.0 Flash), the average is around 2-5%, and the worst exceed 25%.
But those are summarization tasks with source document in front. When the model responds to open questions without a reference document —like when a user asks "what do you think about [your brand]?"— the rates change radically. OpenAI's SimpleQA benchmark shows that some models reach error rates of 75% with barely 1% of abstentions. They almost never say "I don't know".
There is also a counterintuitive piece of data about reasoning models. Models optimized for chains of thought (OpenAI's o3 and o4-mini) hallucinate more in concrete factual questions: o3 reached 33% in PersonQA, double its predecessor o1. Optimizing for complex reasoning seems to push the model to fill factual gaps with plausible conjectures instead of abstaining.
For a CEO worried about how their brand appears in AI responses, this has an uncomfortable implication: that the model is more sophisticated doesn't mean it's more accurate when talking about you. If your information isn't in its sources, sophistication will make it better at inventing, not better at abstaining.
How a hallucination translates into a business problem
The macro figures about losses from AI hallucinations are flashy, but too big to be useful. What matters is understanding the mechanisms by which a hallucination affects your operation.
The phantom competitor. An LLM recommends your product but adds a competitor that doesn't exist or no longer operates. The user compares, doesn't find the competitor and loses confidence in the entire response. Including your mention.
The invented feature. Someone asks "does [your brand] have Salesforce integration?". The model says yes. You don't have it. The lead arrives with an impossible expectation. The damage isn't just the lost opportunity: it's that someone concludes that your brand promises what it doesn't deliver.
The mis-attributed price. The LLM indicates that your service costs €500/month when it actually starts at €2,000. You attract unqualified leads. Your sales team filters more. Your CAC goes up and nobody identifies the cause.
These are not theoretical scenarios. In clients with whom we have worked, the symptom is usually the same before anyone looks at what LLMs say: leads arriving with strange expectations, objections that don't fit the real offer, comparisons against competitors that aren't the usual ones. When the origin is traced, the source is often a generative response. But since nobody monitors those responses, the problem remains invisible.
A hallucination about your brand in an LLM doesn't stay in the LLM. It becomes the lead's expectation, the salesperson's objection and a lost opportunity that you'll never know how to attribute.
How is it corrected? Four layers
There's no silver bullet. What there is is a layered approach that recent research and practice keep validating:
- 01
Active monitoring
Before correcting anything, you need to know what the models are saying. That implies making systematic prompts to the main LLMs with the typical queries of your ICP and recording the responses. There are tools that are starting to automate this (Goodie AI, Scrunch AI, Semrush Enterprise AIO), but the starting point can be manual: ask ChatGPT, Claude, Gemini and Perplexity what they know about your brand and document what they say.
- 02
Reinforcement of the verifiable digital footprint
LLMs prioritize sources with entity authority: verifiable, structured, consistent information present in multiple trusted sources. The weaker your presence in those sources, the more room the model has to invent. In the CREF© framework we call it the Reputation layer: verified presence in directories, specialized media, sector databases, qualified reviews and well-structured proprietary content.
- 03
Content designed for extraction
Your content needs to be optimized not only for Google to index it, but for an LLM to be able to extract factual responses without ambiguity. Autonomous fragments, structured data with schema, questions as headings, and an architecture a model can traverse without losing context. More on AI + GEO.
- 04
RAG and primary sources
RAG (Retrieval-Augmented Generation) allows LLMs to consult external sources before generating a response. It's the most effective mitigation technique documented so far. But it's not foolproof: a paper presented at ICLR 2025 (ReDeEP) demonstrated that hallucinations continue to happen when the model's Knowledge FFNs overweight internal knowledge versus the retrieved external information. If your content isn't among the sources the model consults, RAG doesn't help you.
What we see in companies that discover this
There's a pattern that repeats. We don't present it as baseline with closed methodology, but as something we have observed recurrently in clients in the €3M-15M segment with reasonable digital presence and structured commercial processes:
The sales team starts to notice something strange: leads arrive with expectations that don't fit the offer. Objections that seem to refer to another company. Comparisons against competitors that aren't the usual ones. Nobody knows where they come from.
When we audit what the main LLMs say about the brand —an exercise that's part of what we do in the HSA Protocol— active hallucinations appear. Incorrect prices, non-existent features, wrong positioning against competitors. In one case, a model attributed a client's services to another company with the same name in another country.
The intervention goes through reinforcing the Reputation layer within CREF©: proprietary content structured with extractable fragments, updated schema markup, reinforced presence in sources of sector authority, and periodic monitoring of LLM responses. In cases where the complete process has been executed, the detected hallucinations have decreased in the following months and, above all, the alignment between the lead's expectation and the real offer has improved. We still don't have metrics isolated enough to present it as formal baseline, but the pattern is consistent.
AI Reputation: what changes compared to classic SEO
There's an open debate about whether GEO is a new discipline or an extension of SEO. A recent analysis from Digiday (March 2026) collects the position of veterans in the sector: many GEO tactics are, at the bottom, the same as always —authority, clear content, trust signals.
It's a valid but incomplete reading. What changes isn't so much the tactic as the consequence of the error. In classic SEO, if information about your brand is wrong in some source, the user contrasts it when visiting your website. In GEO, if the LLM has incorrect information, it presents it as truth within the response itself. There's no link for the user to contrast. There's no second opinion in the SERP. The hallucination may be the only version they receive.
That converts hallucination cleanup into a hybrid layer: part GEO (optimization for generative engines), part reputation management (protection of brand perception in channels you don't control). If you want to see how we structure that connection, the GEO page develops the bridge between SEO and AI visibility.
In SEO, incorrect information about your brand is discarded when visiting your website. In GEO, it's presented as truth by the LLM. Without contrast link. Without second opinion. The hallucination may be the only version the user receives.
I think hallucination cleanup will become a recognizable spending category in digital marketing. I don't know how long it will take —it could be a year, it could be three—, but the logic is the same as with online reviews a decade ago: first you ignored them, then you monitored them, and now they're part of the system. The question isn't if it will arrive. It's who will move first.
Conclusion: an acquisition cost that doesn't appear on your dashboard
Brand de-biasing isn't a technical problem that OpenAI, Google or Anthropic engineers will solve. It's a reputation problem that your marketing and growth team will solve (or not).
Each uncorrected hallucination is a worse-qualified lead, an expectation that doesn't fit, a comparison that harms you. And unlike a classic reputation problem, this one is invisible: it doesn't appear in Google Alerts, it doesn't appear in social mentions, your listening tool doesn't detect it. You only see it if you ask the models directly.
The recommendation is simple: start by auditing. Ask ChatGPT, Claude, Gemini and Perplexity what they know about your brand. Compare with reality. If there are discrepancies, you have a problem that is probably already affecting your acquisition. If you want a structured diagnosis, the HSA Protocol includes this AI visibility audit.
Building a digital footprint that LLMs don't need to guess is becoming basic acquisition infrastructure. It won't replace having a good website or good SEO. But it will complement them in a way that, if you ignore it, you'll pay without knowing where.
NEXT READING
If this has made you think you should review what AI says about your brand:
- GEO Glossary — Key concepts: hallucination, Entity Authority, RAG, citability.
- CREF© Framework — Systemic growth framework. The Reputation layer directly addresses this problem.
- HSA Protocol — Diagnosis with generative engine visibility audit.
Growth consulting — To look at it with you with a P&L view.
Related reading
- How to expose data to AI with schema, feeds and entity
- Why AI doesn't recommend the same to everyone
- Elevam Labs public GEO baselines
Shall we work together?
If you want to apply this in your company with a team that combines technical SEO, GEO and paid acquisition measured against the income statement, request a no-commitment audit. You can also check real case studies or read the public GEO baselines that Elevam Labs publishes every quarter.
