Why AI Makes Up Facts and How to Verify Its Answers

Generative AI can produce a polished answer with headings, numbers, and links in seconds. The problem is that the presentation can look more reliable than the substance. A model may combine true facts, outdated information, and an invented detail in one persuasive response. Verification should therefore be part of the workflow, not something you do only when an answer feels suspicious.

What “hallucination” or “confabulation” means

This is a situation in which a system presents a false or unsupported claim as if it were a fact. In its Generative AI Risk Management Framework profile, NIST uses the term *confabulation* and treats it as an information-integrity risk.

The model is not necessarily “lying” in the human sense. It generates a plausible continuation from context and learned patterns. When information is missing, the system may fill the gap instead of stopping.

The most dangerous errors are often not absurd. They may be one wrong number in an otherwise correct paragraph, a fabricated quotation attributed to a real expert, a reference to a document with a plausible title, or a subscription term that has since changed.

When the risk is higher

• The question concerns today’s news, prices, schedules, or software versions.
• You need an exact quotation, legal provision, court case, or research paper.
• The topic is narrow, local, or poorly represented in training data.
• You ask the model to recall a URL without web access.
• The answer requires complicated, multi-step calculations.
• A document contains scanned pages, tables, small print, or diagrams.
• The question is phrased as though a disputed claim has already been proven.

Higher risk does not mean the answer is always wrong. It means the cost of leaving an error unchecked is greater.

A seven-step verification workflow

1. Separate claims from advice

Turn the answer into a list of verifiable claims: names, dates, numbers, product features, and cause-and-effect statements. “This tool is convenient” is an opinion. “This tool supports PDF files up to a particular size” requires official confirmation.

2. Ask the model to identify uncertainty

Tell it to mark what it is unsure about and not to fill gaps. A useful prompt is:

Review the answer and divide its claims into three groups: confirmed by the supplied source, reasonable inferences, and unverified assumptions. If no source is available, do not invent one.

Anthropic’s guidance on reducing hallucinations recommends explicitly allowing a model to say “I don’t know,” finding exact quotations in source material first, and drawing a conclusion only afterward.

3. Request primary sources, but open them yourself

A link in an AI response is the beginning of verification, not evidence by itself. Open the page and confirm that it exists, belongs to the expected organization, contains the relevant claim, and is current.

For a product feature, look for the developer’s documentation. For a law, use an official government source. For a scientific result, read the original publication. For news, find the company statement, filing, document, or direct comment rather than a chain of summaries.

4. Check the date and context

An old page may be genuine but no longer describe the current product. Check its update date, region, plan, platform, and language. Terms for a personal account may differ from Business or Enterprise.

5. Find independent confirmation

For an important claim, find a second reliable source that is not simply copying the first. If two sources disagree, do not automatically choose the one you prefer. Compare the dates, definitions, and methods.

6. Recalculate numbers with a separate tool

A language model may explain a formula, but arithmetic is better checked with a calculator or spreadsheet. For a financial example, write down the inputs, units, and intermediate steps. A well-formatted table is not proof that the numbers are correct.

7. Apply the cost-of-error rule

Your own editing may be enough for a private-message draft. Publication, contracts, medical decisions, cybersecurity, and money require an appropriate professional or an authoritative source. The greater the potential harm, the stronger the review must be.

How to verify an answer based on a PDF

Ask for page references or exact passages, not just a summary. Then open the PDF in a normal viewer and check each quotation in context. Make sure the model has not confused a table heading, footnote, and main body text.

If the document is a scan, inspect the quality of optical character recognition. One misread digit can change a conclusion. Our guide to using AI with PDF files explains the full process.

Warning signs in an AI answer

• A source has a generic title but no author, date, or URL.
• A URL returns a 404 or opens unrelated material.
• Precise percentages appear without a method.
• A quotation attributed to a person cannot be found in the primary source.
• The answer changes after a simple factual clarification.
• The system treats “not found” as “does not exist.”
• Advice is presented as universal even though it depends on the country or circumstances.

A practical prompt template

Answer only from the sources provided below. For each key fact, name the source and quote a short passage that supports it. Label separately any conclusion that is not stated directly. If evidence is insufficient or the sources conflict, say so. Do not create URLs, quotations, or numbers.

Even this prompt does not remove the need for review, but it makes the answer easier to audit.

Conclusion

Reliable AI use is not about finding a model that never makes mistakes. It is about building a process in which claims can be traced to sources, numbers can be recalculated, and uncertainty is visible before a decision is made. Use the model’s speed for analysis and drafts, but do not give it the final word where an error can cause real harm.