AIのハルシネーションとは？回避する方法

Key takeaways

• AI hallucinations are plausible-sounding answers that contain invented or unsupported information.
• Better prompts and reliable source material can reduce hallucinations, but no chatbot is guaranteed to be accurate.
• Verify important claims, quotations, and citations before using AI output in research or decisions.

AI hallucinations happen when an AI system presents invented or unsupported information as if it were factual. You cannot eliminate them completely, but you can reduce the risk by giving the system reliable sources, setting clear limits, and checking its answers.

How it works

A chatbot generates responses using patterns learned during training, along with information available in the current conversation or through connected tools. It produces text that fits the request; that process does not automatically establish whether each statement is true.

This helps explain why a fluent answer can still contain a nonexistent study, an inaccurate product specification, or a made-up quotation.

Hallucinations can arise when:

• Information is missing. The system may fill a gap with a plausible detail instead of acknowledging uncertainty.
• The question contains a false assumption. Asking why an event happened may lead to an explanation even if the event never occurred.
• Sources are unclear or conflicting. The answer may blend incompatible claims into one smooth explanation.
• The task demands unsupported detail. A request for exact dates, quotations, or references can produce inventions when the evidence is unavailable.
• Relevant context is overlooked. Even with a document supplied, the system may misread a passage or miss an important qualification.

Not every incorrect answer is a hallucination. Outdated information, a calculation mistake, or a misunderstanding can also produce errors. For practical purposes, the key question is the same: does reliable evidence support the answer?

Confidence is not a dependable signal. An AI system can sound equally certain when explaining a well-established fact and inventing a reference.

Real-world examples

The following are illustrative situations, not reports of specific incidents.

A chatbot invents a research paper

You ask for studies showing that a particular workplace practice improves productivity. The chatbot returns a convincing title, author list, journal name, and publication year.

The problem: the paper does not exist, or the real paper does not support the claim.

What to do: Search for the title independently. Confirm the authors and publication details, then read the relevant findings. A realistic-looking citation is not evidence.

A summary changes what a document says

You upload a company policy and ask whether contractors receive a particular benefit. The answer says yes, although the policy only mentions full-time employees.

The system has turned an unsupported inference into a factual statement.

What to do: Ask for the exact passage supporting the answer and check it in the original document. If the policy does not address contractors, the appropriate answer is “not specified.”

An assistant invents a software feature

You ask how to export data from an application. The assistant gives detailed instructions for a menu option that does not exist.

What to do: Check the product’s current documentation and your version of the interface. Ask the assistant to distinguish documented steps from suggestions.

A health answer includes unsupported advice

A chatbot provides a precise medication dose without enough information about the person, formulation, or medical circumstances.

What to do: Do not treat generated advice as a clinical instruction. Confirm medication questions with a qualified healthcare professional or pharmacist.

Benefits and limits

AI can still be useful for research and everyday work. It can suggest search terms, explain unfamiliar concepts, organize notes, and help you draft questions to investigate.

These uses are often safer when the output is treated as a starting point rather than a final authority.

Source-based workflows improve accountability. When an answer points to a specific passage, you have something concrete to inspect. Restricting a task to supplied documents can also reduce opportunities for invention.

However, these safeguards have limits:

• Citations can be misleading. A real source may not support the sentence attached to it.
• Web access does not guarantee accuracy. Search results can include outdated, low-quality, or incorrect material.
• Several chatbots agreeing is not independent confirmation. They may repeat the same widespread error.
• Self-checking is imperfect. Asking an AI to review its answer may catch mistakes, but it can also repeat them.
• A reliable source can still be misrepresented. Missing caveats can change the meaning of a research finding.

The consequences should determine the level of checking. Brainstorming a meeting title needs little verification. Medical, legal, financial, or safety-related decisions need authoritative evidence and, where appropriate, professional review.

How to get started

Use this workflow whenever factual accuracy matters.

1. Define the task and its boundaries

Tell the assistant what you need, who the answer is for, and which information it may use.

With ChatGPT, for example, you could supply a document and request a narrowly scoped summary rather than an open-ended explanation.

Avoid uploading confidential, personal, or restricted information unless your organization permits it and you understand the tool’s data controls.

2. Start with evidence

For web research, Perplexity provides source-linked answers that can help you identify material to review. Open the cited pages rather than relying only on the generated summary.

For document-focused research, consider NotebookLM. For help reading and explaining scientific PDFs, SciSpace (Typeset) is another relevant option. Neither replaces checking the underlying text.

3. Explicitly allow uncertainty

Try this prompt:

Answer using only the supplied material. If it does not contain the answer, say “Not enough information.” Separate direct statements from inferences. For each important factual claim, identify the supporting passage. Do not invent quotations or references.

This creates useful boundaries, but it is not a guarantee.

4. Verify the important details

Before sharing or acting on the answer, check:

• Names, dates, figures, and units.
• Whether quotations match the original wording.
• Whether cited sources exist and support the claims.
• Whether information is current enough for your task.
• Whether limitations or conflicting evidence were omitted.

For calculations, recompute the result with a calculator or another appropriate tool.

5. Keep a review trail

Save the source, relevant passage, and any corrections alongside important claims. This makes the work easier to audit, update, and hand over to colleagues.

FAQ

Can you stop AI hallucinations completely?

No. Clear instructions, trustworthy sources, and verification can reduce the risk, but they cannot guarantee error-free output. Design your workflow so that important mistakes are caught before they affect decisions.

Is a chatbot with citations more trustworthy?

Citations make an answer easier to check, which is valuable. They do not prove accuracy: confirm that each source exists, is credible, and supports the specific claim being made.

What should I do when I find a hallucination?

Remove or correct the claim using reliable evidence. Then review nearby claims, especially those that depend on the same source or assumption. Asking the chatbot to revise can help, but verify the replacement rather than accepting an apology as proof of accuracy.