Hallucination is "the risk of AI generating incorrect information about one's own brand," and is a phenomenon that is difficult to eliminate entirely. Here is a summary of the key points.
Key points about hallucination
Point
Details
See section
Types
Four types: factual errors, information confusion, outdated information, and overconfidence
Covered in the table under "What Is Hallucination?"
Effect of maintaining your own site
Can reduce the risk, but cannot eliminate it entirely
Covered under "Example: Higher vs. Lower Hallucination Risk"
Practical countermeasure
Regular monitoring (checking AI query results)
Covered under "What Genview Offers for Hallucination Countermeasures"
In GEO strategy, it is a problem to be addressed through "establishing accurate information + regular monitoring." Genview supports both sides of this through two features: Brand Recognition Improvement, which first defines the ideal image of how you want AI to see you and visualizes the gap with current recognition fact by fact, and Query Improvement, which measures whether AI correctly mentions and references you for targeted queries and provides page-specific fixes.
What You Will Learn From This Page
The meaning, definition, and mechanism behind hallucination
Positioning in GEO strategy
How to think about countermeasures for hallucination
Common misconceptions
What Is Hallucination?
Hallucination is a phenomenon in which an LLM (Large Language Model), when generating "plausible text" based on training data, confidently states information that does not actually exist, is incorrect, or is outdated. It is sometimes translated into Japanese as "genkaku" (幻覚).
LLMs have a mechanism that generates "what is probabilistically the correct next word." As a result, "whether the text reads naturally" may take priority over "whether the information is accurate," and this is one of the causes of hallucination.
The table below summarizes the four main types of hallucination and specific examples from a GEO strategy perspective.
Main types of hallucination and examples from a GEO perspective
Type
Content
Example from a GEO perspective
Factual errors
Generates information that does not exist
Responds that a non-existent feature of one's service "exists"
Information confusion
Mixes and generates information from a different brand or person
Responds with a competitor's characteristics as if they were one's own
Presenting outdated information
Responds with outdated information from the training cutoff as-is
Responds with an already-discontinued plan or old price as current information
Overconfidence
States uncertain information assertively
Responds with an unsubstantiated reputation of one's company as fact
All types can become a risk of AI disseminating incorrect information about one's company. From a GEO strategy perspective, establishing accurate information and regular monitoring are important for reducing these risks.
Example: Higher vs. Lower Hallucination Risk
This table compares how the state of information preparation on one's own site affects AI hallucination risk.
Differences in site information preparation and hallucination risk
Site status
Situation
Impact on AI responses
❌ Higher risk
The definition, features, and target users of one's service are written vaguely on the site, and information is inconsistent
AI may not be able to accurately understand "what this service is" in its training data, potentially making hallucination more likely to occur
✅ Lower risk
The service definition, features, and target users are clearly described, and the same information is consistently mentioned in external media
Accurate information for AI to reference is established, potentially reducing the risk of hallucination
The key is that consistent information is established not only on one's own site, but also in external media. Since AI training data is composed of many sources, consistency of information externally also affects risk reduction.
Genview's Definition of Hallucination
In the context of GEO strategy, Genview defines hallucination as "the risk of AI generating incorrect information about one's own brand — a problem to be addressed through establishing accurate information and regular monitoring."
This definition of hallucination as a risk to be addressed through accurate information and regular monitoring represents Genview's perspective and does not reflect an industry-wide consensus.
Genview's adoption of this positioning — treating hallucination as a problem to be addressed through establishing accurate information and regular monitoring — is based on three points.
1
Richness of brand information in training data
It is believed that LLMs are more likely to produce hallucinations when information about one's brand in the training data is scarce, ambiguous, or inconsistent. Consistent information preparation on the web (clarifying definitions and strengthening entity recognition) may lead to reduced hallucination risk. However, this is Genview's inference as of May 2026 and has not been officially disclosed by any of the companies involved.
2
Grounding of sources through RAG and Grounding
RAG and Grounding function as means of reducing hallucination by having LLMs respond based on specific sources as a basis (Lewis et al., 2020). Establishing accurate content that AI can reference may also contribute to hallucination reduction via RAG and Grounding.
3
Detection through regular monitoring
Hallucination cannot be noticed without regularly checking "what AI is saying about one's company." Monitoring AI query results is positioned as a practical means for hallucination detection.
What Genview Offers for Hallucination Countermeasures
The basics of hallucination countermeasures are "establishing accurate information" and "regular monitoring," but manually and continuously checking five AIs (ChatGPT, Gemini, Perplexity, Claude, and Grok) is not realistic.
Genview's Brand Recognition Improvement feature visualizes how these major generative AIs understand your brand, and lets you check the gap with your ideal image fact by fact. Specifically, it captures your recognition status through the following flow.
What Brand Recognition Improvement lets you do
Capability
Details
Define your ideal image
Set a clear brand positioning for how you want AI to perceive you
Visualize how major AIs see you
See a summary of how ChatGPT, Gemini, Perplexity, and other major generative AIs actually understand your brand
Score the gap fact by fact
Each fact in your ideal image is judged as accurately recognized, partial/vague, or misrecognized
Get auto-generated fixes
Get concrete suggestions for what to add, and to which page, to close the gap
You can currently check your status through two numbers: recognition match rate (shows, as a percentage, how accurately AI recognizes you against your ideal image) and misrecognition count (the number of items where AI is describing you in a way that's clearly factually wrong). Since this gives you an actual number rather than a vague sense that things "seem about right," it supports both regular monitoring of whether hallucination is actually occurring and prioritizing what to fix.
Hallucination is a phenomenon arising from LLMs' probabilistic text generation mechanism, and is positioned in GEO strategy as a problem to be addressed through maintaining accurate information and monitoring. The following organizes the concepts related to hallucination.
Parent Concepts
LLM (Large Language Model): Hallucination is a phenomenon arising from LLMs' probabilistic text generation mechanism.
GEO (Generative Engine Optimization): Addressing hallucination is one of the important aspects of GEO strategy. Maintaining accurate information and monitoring are its practice.
Related Terms
Grounding: The mechanism by which AI grounds its responses based on specific information sources. Grounding is one of the representative means for reducing hallucination.
RAG (Retrieval-Augmented Generation): The mechanism by which AI searches for and retrieves external information before generating a response. RAG is expected to supplement the limitations of LLMs' learned knowledge and reduce hallucination.
Entity: A target that AI and search engines recognize as "a concept, thing, person, or organization with distinct meaning." Having a brand accurately recognized as an Entity may contribute to reducing the risk of hallucination regarding the brand.
Citation: The citation or mention of a brand's content in AI responses. When AI is generating incorrect information, monitoring citations may allow detection of hallucination occurrences.
Fact-checking: The act of verifying information accuracy. Functions as a practical means for confirming whether hallucination is contained in AI-generated responses.
Common Misconceptions
The following three misconceptions about hallucination are frequently observed.
Misconception 1: "Hallucination is an AI bug."
Hallucination is not a design defect in LLMs, but a phenomenon arising from the mechanism of probabilistic text generation. Since LLMs generate prioritizing "whether the text reads naturally" over "whether the information is accurate," they may confidently state incorrect information in areas where training data is insufficient. It is difficult to eliminate entirely, and is something to be addressed through RAG, Grounding, and regular monitoring.
Misconception 2: "Maintaining one's own site will eliminate hallucination."
Maintaining information on one's own site is one means of reducing hallucination risk, but does not eliminate it entirely. AI training data is composed of many sources, and incorrect information from sources other than one's own site may also influence AI. Additionally, for direct questions to LLMs that do not use RAG or Grounding, responses are generated from learned knowledge alone, so cases may arise where the latest information on one's own site is not reflected.
Misconception 3: "Users will notice hallucination."
Whether hallucination is occurring cannot be noticed without someone who accurately knows one's brand information. General users tend to trust AI responses, creating a risk that incorrect information will spread. It is important for the brand side to regularly check and monitor AI responses.
FAQ
Q: What should I do to reduce hallucination about my company?
Initiatives considered effective at this time include: 1) clearly describing one's service's definition, features, and target users; 2) establishing author and organizational information to clarify entities; 3) accumulating consistent information mentions in external media; and 4) regularly searching for one's own queries in AI and checking response content. However, direct causal relationships have not been confirmed as of May 2026.
Q: How can I check whether hallucination is occurring?
A practical approach is to directly ask about one's brand name, service name, and representative names on services such as ChatGPT, Claude, Gemini, and Perplexity, and check whether the response content is accurate. Regular checking allows one to notice the occurrence of hallucination and outdated information.
Q: Will hallucination disappear if RAG is used?
RAG is expected to have the effect of reducing hallucination, but does not eliminate it entirely. There are cases where the source used for RAG itself contains errors, or where AI misinterprets the source. RAG is a mechanism for "providing more accurate information as a basis," and does not guarantee accuracy.