AI hallucination refers to the phenomenon where generative AI outputs information that is plausible but differs from the facts or is contextually inappropriate. Generative AI constructs sentences by probabilistically calculating "plausible word connections," and does not judge the accuracy of the facts. Especially in companies, the generation and dissemination of non-existent scandals or incorrect management information can directly lead to serious risks such as brand degradation and decision-making errors. This article systematically organizes five areas: causes, types, risks, examples, and countermeasures.

What is AI Hallucination?

AI hallucination refers to the phenomenon where generative AI outputs information that is not based on facts, confidently presenting it as if it were correct.

As the etymology of the word "hallucination" suggests, it is named for the way AI behaves as if it "sees" things that do not exist. It is considered one of the biggest challenges of generative AI.

Meaning and Etymology of Hallucination

  • "Hallucination" means "a false perception" in English.
  • Unlike mere typos or omissions, the content itself diverges from the facts.
  • Because it is output in a plausible tone, users without specialized knowledge may find it difficult to notice the errors.

Specific Examples of Hallucination

  • Citing non-existent papers or case law as if they "exist."
  • Generating incorrect backgrounds or statements about real individuals.
  • Misidentifying current officeholders or figures based on outdated training data.

What Types of Hallucination Exist?

Hallucinations can be broadly classified into two types: "Intrinsic" and "Extrinsic."

They are distinguished based on whether they contradict the training data or produce information that does not exist in the training data, leading to different approaches for countermeasures.

Intrinsic Hallucinations

  • Outputs that contradict information contained in the training data.
  • Example: Generating different figures despite having learned the correct ones.
  • Errors in data reference or failures in understanding context are considered contributing factors.

Extrinsic Hallucinations

  • Outputs that "create" information that does not exist in the training data.
  • Example: Generating non-existent statistical data or market sizes.
  • It is believed that this type occurs more frequently with proper nouns, up-to-date information, and niche fields.

What Causes Hallucinations?

The main causes of hallucinations can be summarized into three categories: "bias or lack of training data," "ambiguity in prompts," and "structural limitations of the AI model."

These factors act not in isolation but in combination, increasing the probability of generating misinformation.

Bias or Lack of Training Data

  • The AI has not learned correct knowledge or has strong specific biases.
  • The collection point of the training data is outdated, diverging from current facts.
  • Specialized fields and niche areas tend to have insufficient data.

Ambiguity in Prompts

  • The AI cannot correctly understand the intent of the instructions.
  • Insufficient prerequisites lead the AI to "supplement" information.
  • Questions may be too complex or too abstract.

Structural Limitations of the AI Model

  • Generative AI probabilistically selects "the next word" to construct sentences.
  • It fundamentally lacks a mechanism to verify the accuracy of facts.
  • Insufficient grounding (linking to the real world) increases errors.

If you want to understand the mechanism of AI search itself, you can deepen your understanding by checking Terminology and Overview of AI Search Countermeasures.

What are the Risks of Misinformation About Companies Being Disseminated?

When misinformation about companies is spread due to hallucinations, three risks arise: loss of social trust, economic losses, and security risks.

Particularly, proper nouns such as company names and management information are considered areas where hallucinations are likely to occur.

Loss of Social Trust and Brand Degradation

  • Non-existent scandals or management situations that differ from the facts are generated.
  • These are disseminated on social media and the web, damaging the corporate brand.
  • Example: Generation of false information stating "a certain company caused a data leak."

Economic Losses Due to Incorrect Decision-Making

  • Employees may uncritically use AI's misinformation in management decisions.
  • Plans may be based on non-existent market data or technical specifications.
  • The collapse of premises for marketing strategies could lead to losses.

Information Security and Intellectual Property Risks

  • Inputting confidential information into prompts raises concerns about information leakage.
  • Incorrect citations may pose a risk of infringing on other companies' intellectual property.
  • There is a possibility of developing legal liabilities and compliance issues.

Countermeasures for B2B companies to prevent the dissemination of incorrect information in AI searches are detailed in B2B Companies' AI Search Countermeasure Strategies.

What are Specific Examples of Hallucination?

There have been reports of incidents where lawyers cited non-existent case law and generated misinformation about real individuals, leading to actual harm.

These are considered typical examples where "plausibility" led to misrecognition.

Example of a Lawyer Citing Non-Existent Case Law

  • Generative AI created fictional case law that was cited in court documents.
  • In reported cases, six fictional cases were cited.
  • The difficulty for even experts to notice the errors was highlighted as a problem.

Example of Generating Misinformation About Real Individuals

  • The AI output false information about a real person.
  • There are concerns that this could lead to defamation.
  • Outputs related to proper nouns are particularly in need of verification.

Examples of Incorrect Responses in Medical and Financial Fields

  • Incorrect responses in critical decision-making areas can lead to serious consequences.
  • In medical and financial fields, verification of accuracy is deemed essential.
  • Insufficient grounding is considered a contributing factor to incorrect responses.

What are the Countermeasures for Hallucination?

The basics of countermeasures against hallucinations include "human checks," "utilization of RAG," "refining prompts," and "establishing guidelines."

Since hallucinations cannot be completely eliminated, it is essential to manage risks while utilizing them.

Make Prompts Clear and Specific

  • Present prerequisites and background information concretely.
  • Specify that "if there is no information, respond with 'I don't know'."
  • Break down questions and limit the scope of responses.

Thorough Fact-Checking (Human in the Loop)

  • Ensure that the output is verified by a human.
  • Cross-check with primary information and information from public institutions.
  • Particular emphasis should be placed on verifying proper nouns, figures, and citations.

Utilize RAG (Retrieval-Augmented Generation)

  • Have the AI reference external accurate databases.
  • Encourage the generation of evidence-based responses and suppress fabrication.
  • It is important to organize accurate information from your company as a reference source.

Designing AI to accurately cite your company information is explained in detail in Designing Sites for Accurate AI Citations.

Establish Guidelines and Manuals

  • Document internal rules for AI usage.
  • Define whether confidential information can be input.
  • Thoroughly inform users about risks.

Comparison Table of Hallucination Countermeasures

The main countermeasure methods can be organized by purpose, immediacy, and difficulty as follows.

Countermeasure Method Main Purpose Immediacy Implementation Difficulty
Refining Prompts Eliminating Ambiguity in Instructions High Low
Fact-Checking Identifying Misinformation High Low
Utilizing RAG Generating Evidence-Based Responses Moderate High
Establishing Guidelines Organizational Risk Management Moderate Moderate
Checking Training Data Correcting Bias and Insufficiency Low High

The Importance of Accurately Recognizing Company Information by AI

To prevent the spread of misinformation due to hallucinations, it is effective for companies to accurately organize the primary information that AI references.

As the AI mode of Google search becomes widespread, accurately disseminating company information is increasingly important as part of risk management. Details of the mechanism can be checked in Understanding the Mechanism of Google Search "AI Mode".

  • Organize structured information that is easy for AI to reference.
  • Clearly state accurate facts in an FAQ format.
  • Continue to disseminate official information as the primary source.

Frequently Asked Questions (FAQ)

Q1. Can AI hallucination be completely eliminated?

Complete elimination is considered difficult. Since generative AI has a structure that probabilistically generates text, hallucinations are seen as "something that cannot be completely eliminated," making it essential to manage risks through human checks and the use of RAG.

Q2. Why is misinformation more likely to occur with company names and proper nouns?

Proper nouns and up-to-date information are often less represented in training data, making it easier for AI to "supplement" and create information. This is considered an area where extrinsic hallucinations are likely to occur, making verification of output results particularly important.

Q3. What should be the first step in countering hallucinations?

It is recommended to start with "clarifying prompts" and "thorough fact-checking," which have high immediacy and low difficulty. After that, it is realistic to gradually expand to organizational measures such as utilizing RAG and establishing guidelines. For information organization in FAQ format, How to Create FAQs that are Cited in AI Searches can be a useful reference.

Conclusion: Addressing Hallucination Through Risk Management

AI hallucination is an inevitable phenomenon stemming from the structural characteristics of generative AI, directly linked to the risk of misinformation about companies being disseminated.

What is important is not to aim for "complete elimination," but to understand the causes (training data, prompts, model structure) and combine fact-checking, RAG, and guideline establishment to minimize risks. Especially for companies, accurately organizing the primary information that AI references and maintaining a stance that does not create a starting point for misinformation dissemination will become the foundation of reliability in the coming AI era.

Author Information This article is written by an editorial team that deals with information on AI search optimization (LLMO/GEO) and risk management of generative AI, referencing public institutions and various guidelines. The content is general information as of June 2026, and individual judgments should be consulted with experts.