Causes and Prevention of Hallucinations in Generative AI: What You Need to Know to Ensure Accuracy

Generative AI is being widely utilized in diverse applications such as text generation, question answering, and image creation. It is increasingly integrated into daily operations, educational environments, and welfare sectors. However, one of the most frequently criticized issues is the occurrence of “hallucinations”—a phenomenon where AI generates false content that appears to be accurate. This article explores the causes behind hallucinations and outlines effective strategies to prevent them.

Who Will Benefit from This Information

This article is particularly useful for the following individuals:

• Developers and project managers aiming to implement generative AI in business settings
• Media professionals and writers using AI to generate articles and content
• Educators and researchers considering AI-based educational material development
• Public relations officers in government or welfare institutions striving for accessible communication
• Professionals in fields like law, healthcare, and finance where information accuracy is critical

As AI adoption spreads across various fields, having a foundational understanding of “accurate and trustworthy AI usage” is crucial for everyone.

What is a Hallucination?

A hallucination refers to the phenomenon where AI generates information that is not based on facts and presents it in a seemingly credible manner. For example, citing non-existent academic papers or attributing quotes to fictional individuals. Unlike misinformation, these are AI-generated “lies” logically constructed within its internal model, making them difficult to detect.

This phenomenon is particularly prone to occur in the following scenarios:

• When the AI is forced to answer questions with no definitive answer
• When extrapolating beyond its training data
• When given ambiguous instructions or prompts

Hallucinations are especially dangerous in high-risk fields (medicine, law, finance, education), making cautious usage essential.

Why Do Hallucinations Occur in Generative AI?

Generative AI operates by predicting the next word or sentence pattern based on large amounts of previously learned text data. This approach, rooted in “probabilistic language modeling,” aims to generate the most likely response given the context.

In other words, AI doesn’t “know” the correct answer—it specializes in producing plausible language patterns. This approach has several limitations:

• No capability for fact-checking: AI does not verify information against databases or real-time data during generation.
• Ambiguous data sources: The origins of the data used during training are often unclear, making it hard to trace the source.
• Lack of consistency in long-form content: The longer the generated text, the more likely it is to contain inaccuracies to maintain coherence.
• Ambiguity in language: Natural language contains many interpretable expressions, which can lead AI to misunderstand context.

These factors combine to cause AI to generate information that seems true but is actually false—a “hallucination.”

Specific Measures to Prevent Hallucinations

To reduce the risk of hallucinations, careful consideration is required in AI design, deployment, and prompt formulation.

1. Clear and Specific Prompt Design

Avoid ambiguous instructions by framing them in a specific and structured manner:

Bad Example: “Tell me about the evolution of AI”
Good Example: “List three major generative AI models released after 2024 and their social impacts”

2. Integration with External Databases and Search Tools

Pairing AI with reliable databases or search engines can improve information verification. Using web search for real-time data supplementation is particularly effective.

3. Incorporation of Fact-Checking Mechanisms

Implement a system where human reviewers or specialized verification algorithms fact-check AI-generated content. This is especially critical in legal and academic applications.

4. Habitual Use of Citations and Source Verification

Encourage the use of prompts that demand citations and verify whether those sources actually exist.

Example: “Please introduce only real research papers, with citations.”

Conclusion: Toward Trustworthy Use of AI

The evolution of generative AI is remarkable, and its convenience continues to grow. However, unless we confront the issue of hallucinations directly, its use could lead to the spread of misinformation. A responsible and well-informed approach is essential to unlock the full potential of AI while minimizing risks.