As generative AI rapidly expands into business, service, and decision-making, cases where "AI speaks plausibly but actually gives the wrong answer" are becoming more frequent. This phenomenon is not a simple error, but rather a structural problem known as hallucination. Hallucination directly undermines the trustworthiness of AI adoption and, especially in corporate environments, can lead to legal, financial, and reputational risks. Therefore, hallucination should be viewed not as a technical defect, but as a design and operational issue.

What is Hallucination?

Hallucination refers to the phenomenon in which AI generates information that appears to be factual but is unsubstantiated or incorrect. In these cases, the AI ​​doesn't signal its ignorance; rather, it often outputs results in the form of highly confident sentences. The core of the problem isn't the "wrong answer," but rather the attitude of presenting the wrong answer as if it were correct.

Structural causes of hallucination

1. Limitations of learning methods

Large-scale language models are not systems that retrieve facts, but rather systems that predict the next word probabilistically. In other words, they only generate the most plausible response based on context, without independently verifying its authenticity. This structural characteristic is the root cause of hallucination.

2. Data gaps and incompleteness

When AI is asked questions that require up-to-date information, insider information, or specialized documentation, rather than simply saying "I don't know," it tends to combine existing patterns to generate answers. This dramatically increases the likelihood of hallucination.

3. Problems with question design

Ambiguous or incorrectly premised questions can lead AI to generate incorrect answers. In particular, requests like "Explain as if..." or "Give me the exact number" can lead to unsubstantiated claims being presented as fact.

4. Contextual disconnection and memory errors

In conversational environments, if previous context isn't properly maintained or long passages are truncated, AI can generate inconsistent information. This is perceived as even more subtle hallucination from the user's perspective.

Areas where hallucination is particularly dangerous

Hallucinations are a problem in all fields, but they are particularly pernicious in the following areas, where even “plausible errors” are not tolerated:

• Law, Regulation, and Compliance
• Medical, Insurance, and Financial Decision-Making
• Internal corporate policy and contract information
• Official content for external communications

Key principles for dealing with hallucinations

1. Define AI as an "inference tool," not a "source of knowledge."

The first step in responding is to clearly define the role of AI. AI should not be used as the final arbiter of facts, but rather as a tool to organize information and assist in reasoning. Without this premise, all response strategies will be ineffective.

2. Design a source-based response structure.

When AI generates answers, it can be restricted to relying on internal data or trusted sources. It's important to design it so that it doesn't generate answers without a basis. This is one of the most effective ways to reduce hallucinations.

3. Utilization of the RAG (Retrieval-Augmented Generation) structure

The RAG structure allows AI to retrieve information from pre-verified data and then generate responses, rather than generating answers randomly. When using AI in a corporate environment, RAG is not optional, but practically essential.

4. Specify the conditions under which you can say “I don’t know.”

AI doesn't need to answer every question. Rather, clearly defining criteria for not answering increases trust. AI should be designed to respond with "Unable to confirm" or "Additional information needed" when a certain confidence score is below or when there's insufficient evidence.

5. Includes a human-in-the-loop structure.

Critical outcomes must include a human verification process. The scope of AI's sole judgment must be limited, and high-risk areas must be prevented from being exposed externally without human approval.

Response strategy from an operational perspective

1. Log and feedback loop design

Hallucination cases should be documented and continuously analyzed to determine the questions and context in which they occurred. This will serve as key data for model improvement, question guidance, and system design improvements.

2. The Importance of User Education

Making AI users clearly aware of its limitations is also an important strategy. The moment AI is used as a search engine or expert, the risk of hallucination increases dramatically.

Insight Summary

Hallucinations are not temporary errors in AI, but stem from the structural characteristics of probabilistic generative models. Therefore, countermeasures cannot be addressed with technology alone. Role definitions, data structures, question design, and validation processes must be designed together. The key to trustworthy AI is not to perfect it, but to design systems with the assumption that it can be wrong. When this premise is maintained, AI becomes a trustworthy tool, not a risk.