AI hallucinations — why can AI sound certain and still be completely wrong?

1. Introduction: AI’s most convincing mistake

AI’s most dangerous mistake doesn’t always look like a mistake.

It isn’t necessarily a clumsy sentence, an odd answer or an obviously absurd claim. Often it’s the opposite: a fluent, expert-sounding, confident answer that seems entirely credible at first glance.

That’s exactly where the problem lies.

AI can describe a study that doesn’t exist, invent a citation, mix up a person’s background, give a wrong legal interpretation, treat a health issue too lightly or build a convincing strategic analysis on a faulty assumption.

This is commonly called an AI hallucination.

The term isn’t perfect, because AI doesn’t hallucinate the way humans do. It doesn’t experience anything, and it has no consciousness. Still, the word has become the established way to describe situations in which AI produces plausible-sounding but incorrect, misleading or entirely fabricated content.

OpenAI itself advises in its guidance that ChatGPT should be used as a first draft, not a final source. Quotes, data, technical details and references to external documents in particular should be verified against reliable sources yourself.^[1]

This is a good basic rule for the entire AI era:

AI can help us think faster, but it doesn’t free us from checking.

2. What does AI hallucination mean?

An AI hallucination is a situation in which AI produces an answer that is:

• factually wrong
• misleading
• fabricated
• unsourced but confident-sounding
• inconsistent with the material provided
• partly true but wrong in essential ways
• oversimplified to the point that the meaning changes

NIST, the U.S. National Institute of Standards and Technology, uses the following term in its generative AI risk report: confabulation. It refers to a phenomenon in which a generative AI system confidently produces and presents erroneous or false content. According to NIST, such errors stem from the basic logic of generative models: they produce answers by predicting likely words, structures and continuations based on their training data.^[2]

Put more simply:

AI doesn’t always know when it doesn’t know.

It can fill gaps with fluent text. And because the text sounds good, people can mistake it for knowledge.

That’s what sets an AI hallucination apart from an ordinary error. An ordinary error may be easy to spot. A hallucination, by contrast, often blends in with accurate information.

It’s like a report that is 90 percent correct and 10 percent made up. The problem is that the reader has to identify exactly that ten percent.

3. Why does AI hallucinate?

You can’t really understand AI hallucinations without understanding one basic fact: a large language model is not a database.

By default, it doesn’t retrieve an answer the way a person would look something up in a source or an expert would recall a verified fact. A language model generates text by predicting which word, sentence or structure is most likely to come next.

That makes it an incredibly effective writer, summarizer and idea generator. But it also makes it risky for factual work.

Hallucinations arise for reasons such as these:

1. The model fills in gaps

Even when the model has no reliable information, it can still produce an answer that sounds plausible.

2. Training data can be incomplete or contradictory

The model has learned from an enormous amount of text. Not all of that text is true, current or high quality.

3. The model doesn’t always distinguish fact from style

If a claim sounds academic, journalistic or official, the model can reproduce that style without the content being true.

4. The user’s prompt can steer it in the wrong direction

If a question contains a false assumption, the AI may build on it. For example:

“Why did the Finnish startup X go bankrupt in 2023?”

If the company didn’t go bankrupt, a good system would correct the assumption. A weaker answer may start explaining a bankruptcy that never happened.

5. Evaluation methods can reward guessing

According to OpenAI researchers, one cause of hallucinations is that models are often evaluated in ways that reward answers that look correct and penalize expressions of uncertainty. If a model learns that guessing earns a better score than “I don’t know,” it may behave like a student on a multiple-choice exam: a confident guess beats a blank answer.^[3]

This is an interesting and somewhat uncomfortable finding. We’ve built systems that sound smart. Now we also have to teach them to be honestly uncertain.

4. Why are hallucinations so dangerous?

Hallucinations are dangerous precisely because they don’t feel dangerous.

When AI answers quickly, clearly and confidently, the user’s threshold for checking the answer drops. This is especially problematic in busy workplaces, where people are looking for quick decisions, summaries and recommendations.

A hallucination can cause harm in at least five ways.

1. Wrong decisions

A company may make a decision based on incorrect market data, a made-up customer segment or a flawed competitor analysis.

2. Reputational damage

If an organization publishes incorrect AI-generated information, trust suffers quickly.

3. Legal risks

A wrong legal interpretation, an invented contract clause or an incorrect reference to regulation can have serious consequences.

4. Security risks

In cybersecurity, an incorrect AI answer can send an analyst chasing a false alarm or let a real threat go unnoticed. In its generative AI risk list, OWASP highlights the problem of misinformation and overreliance: hallucinations can look correct even though they are fabricated.^[4]

5. Distortion at the societal level

If AI systems start relaying news, research or public-authority information incorrectly, the impact doesn’t stay at the individual level. In 2025, Reuters reported on a study in which nearly half of leading AI assistants’ answers about the news contained significant errors, with many of the problems related to sourcing.^[5]

This reveals the big societal question behind hallucinations. When AI becomes a gatekeeper of information, an error is no longer just an error. It can shape what people believe to be true.

5. The different forms of hallucination

Not all hallucinations are alike. Some are minor inaccuracies, others serious fabrications.

5.1 Invented facts

AI may claim that a person said something, that a company received a certain amount of funding or that a study was published in a certain journal, when none of that is true.

5.2 Invented sources

One classic hallucination is a nonexistent citation: an article, book, study or court case that looks genuine but doesn’t exist. This is especially dangerous in academic, legal and journalistic work.

5.3 Incorrectly combined information

AI can combine real facts in the wrong way. For example, the right person, the right company and the right event can get mixed up with one another.

5.4 Outdated information

A model may give an answer that was once true but no longer is. This is common in areas such as:

• regulation
• prices
• products
• company leadership
• political offices
• funding rounds
• technology features

5.5 Overconfident interpretation

AI can draw an overly certain conclusion from something uncertain. For example, it might conclude from customer feedback that “customers want feature X,” even though the data shows only a handful of user comments.

5.6 Contradicting the provided material

Even when a user provides a document for analysis, the model may still claim the document contains things it doesn’t. This matters especially in business use. If AI summarizes contracts, requests for proposals or board materials, the user needs to make sure the summary is actually based on the material.

6. Why even source citations don’t always save you

A common piece of practical advice goes like this:

“Ask the AI to add citations and check them yourself.”

That’s good advice, but it comes with an important follow-up: don’t trust citations just because they look like citations.

AI can make three kinds of sourcing errors:

1. The source is entirely made up

The citation looks credible, but the article, report or study doesn’t exist.

2. The source exists but doesn’t support the claim

This is a more insidious error. The link or study may be real, but the AI claims it says something it doesn’t.

3. The source is outdated or used out of context

AI may rely on a source that no longer applies or that relates to a different country, industry or situation.

That’s why checking sources requires three things:

• open the source yourself
• find the passage the claim is based on
• assess whether the source is reliable and up to date

OpenAI recommends verifying quotes, data, technical details and references to external documents in particular, and using search or other tools when accuracy matters.^[1]

Here’s a good rule of thumb: A citation from AI is a clue, not proof.

7. Hallucinations at work and in business

At work, AI is often used in exactly the places where hallucinations can do the most damage:

• summarizing reports
• market analysis
• customer communications
• in recruiting
• drafting legal texts
• technical documentation
• explaining code
• preparing decision materials
• interpreting internal guidelines

The problem isn’t that AI makes mistakes. Every tool makes mistakes. The problem is that AI can make mistakes fluently.

A bad Excel formula might produce a strange result. A bad AI answer can produce a well-written justification for the wrong result.

This changes the logic of management and quality assurance. In a company, it’s not enough to tell employees, “Use AI wisely.”

You need to define:

• what AI may be used for
• what it may not be used for
• when sources must be checked
• when an expert must approve the final result
• what information may be fed into AI
• how errors are reported
• who is responsible for information that is published or used

NIST’s generative AI risk profile places hallucinations within a broader framework of reliability, safety and explainability. So this isn’t just about an isolated “the AI messed up” moment — it’s about organizational risk management.^[2]

8. Startups, speed and the risk of false information

Startups love speed. And for good reason.

With AI, a small team can produce market research, pitch decks, customer messages, competitor analyses and product ideas at a pace that not long ago would have taken far more time.

But speed is exactly what makes hallucinations dangerous.

If an AI-generated competitor analysis contains invented features, a startup may build the wrong positioning. If an incorrect market size ends up in investor materials, credibility crumbles. If a customer message promises a feature the product doesn’t have, a trust problem follows.

In a startup’s day-to-day work, it pays to treat AI as a sparring partner, not an automatic truth machine.

Good use:

“Give me hypotheses for why customers aren’t converting.”

Risky use:

“Tell me why customers aren’t converting” — without any data.

Good use:

“Draw up a list of questions we can use to validate this market assumption.”

Risky use:

“Confirm that this market is growing.”

That’s a big difference. AI is excellent at generating questions and options. But if you ask it for certainty on something that requires data, it can give you a feeling of certainty without any basis for it.

9. How can you reduce hallucinations?

You can’t eliminate hallucinations entirely with a good prompt alone, but you can reduce them significantly.

9.1 Give the AI a clear task

A vague prompt invites more guessing.

Bad:

“Tell me about this company.”

Better:

“Summarize only on the basis of the text below. If a piece of information can’t be found in the text, say ‘not mentioned in the material.’”

9.2 Ask it to express uncertainty

It’s worth instructing the AI to say what it’s sure about and what it isn’t.

“In your answer, separate facts, assumptions and uncertain points.”

9.3 Restrict the sources

If you want a fact-based answer, specify the sources.

“Use only the sources I’ve provided. Don’t add outside claims.”

9.4 Be careful when asking for direct quotes

Quotes are prone to hallucination. If you need an exact quote, check it against the original source.

9.5 Use an “unknown” instruction

A good prompt might include this:

“If you don’t know, or the source doesn’t support the claim, say so directly. Don’t guess.”

9.6 Do a second review pass

Ask the model to check its own answer:

“Review your previous answer critically. Which claims need a source? Which might be uncertain?”

This doesn’t replace human review, but it helps you find the risky spots.

9.7 Use reliable external sources

When it comes to current information, an AI answer must be checked against primary sources, such as:

• public authorities
• companies’ own press releases
• scientific publications
• statistical sources
• legitimate news outlets
• standards organizations
• original reports

This sounds slow. But fixing a mistake after the fact is often slower.

10. A practical checklist for evaluating AI answers

When you get an answer from AI, go through these questions.

Facts

• Does the answer include names, years, figures or percentages?
• Is there a source for them?
• Does the source actually support the claim?
• Is the information up to date?

Sources

• Do the sources exist?
• Are they primary or secondary?
• Is the source reliable?
• Is the source from the right country and the right context?

Logic

• Is the conclusion based on the data provided?
• Are there alternative explanations?
• Is the answer overconfident?
• Are fact and interpretation getting mixed up?

Use case

• Could an error cause financial, legal, health-related or reputational harm?
• Is an expert review needed?
• Can this be used as a draft, or as the basis for a decision?

Publishing

• Would you publish this under your own name?
• If someone asks for the source, can you find it?
• Does the text contain claims you can’t defend yourself?

Here’s a simple but effective principle: don’t publish an AI claim that you can’t understand or verify yourself.

11. Good prompts for reducing hallucinations

Here are practical prompt templates you can use at work.

11.1 Fact-checking prompt

Check the following text from a factual standpoint.
Sort the claims into three groups:

1) probably correct
2) needs a source
3) possibly incorrect or misleading

Don’t make up sources. If no source has been provided, say “source missing.” Finally, suggest which points I should verify against primary sources.

11.2 Source-discipline prompt

Answer only on the basis of the sources provided below.
If the sources don’t contain the answer, say: “The answer can’t be found in the sources provided.”
Don’t use general knowledge, don’t guess and don’t fill in gaps.

11.3 Uncertainty prompt

Give your answer, but label each key claim as either:

• fact
• interpretation
• assumption
• uncertain

Finally, list three things that should be checked before the answer is used for decision-making.

11.4 Decision-making safety prompt

Act as a critical analyst.
Evaluate the AI-generated answer below.
Look for possible hallucinations, overconfident conclusions, missing sources and false assumptions.
Don’t polish the text yet. Give me the list of risks first.

11.5 Pre-publication check

Assess whether the following text can be published on an expert blog.
Flag the passages that need a source, clarification or softening.
Suggest how the uncertain claims could be phrased more responsibly.

12. Ground rules for organizations: who is responsible for AI’s mistakes?

AI doesn’t take responsibility. People and organizations do. That’s a harsh but necessary starting point.

If a company publishes an incorrect AI-generated claim, the customer usually doesn’t think, “Well, the AI did it.” They think the company didn’t check.

That’s why organizations need clear ground rules.

12.1 Low-risk use

AI can be used more freely for things like:

• brainstorming
• drafting
• bouncing ideas around internally
• summarizing text when a person reviews it
• planning structure
• generating alternative headlines

12.2 Medium-risk use

Review is needed when AI is used for:

• customer communications
• sales materials
• recruiting copy
• internal guidelines
• draft reports
• market analyses

12.3 High-risk use

Expert approval is needed if the topic involves:

• law
• health
• safety
• investing
• personal data
• regulatory requirements
• contracts
• cybersecurity
• critical business decisions

A good AI policy doesn’t prevent use. It makes use safer. It tells employees where AI is useful and where they need to be especially careful.

13. The future: can hallucinations be eliminated entirely?

The short answer: not yet, at least.

Hallucinations are tied to the fundamental nature of generative AI. Models produce probable answers, not automatically verified truths. But the risk can be reduced.

Future solutions will likely be built in several layers:

1. Better models

Models are learning to express uncertainty better and to decline to guess.

2. Better evaluation methods

If systems are rewarded for saying “I don’t know” at the right moment, they may hallucinate less. According to OpenAI researchers, current evaluation methods can encourage guessing, so changing the metrics is an important part of the solution.^[3]

3. Retrieval-based systems

When AI is connected to reliable sources, document search and databases, it can ground its answers in material that is easier to verify. This doesn’t eliminate errors either, but it makes them easier to spot.

4. More visible use of sources

Users need to see what an answer is based on. A good AI system doesn’t just say “this is how it is” — it shows where the information comes from.

5. Human oversight

In critical use cases, human review won’t disappear. It will become even more important.

So the goal for the future isn’t blind automation. The goal is trustworthy collaboration, in which AI speeds up the work but people retain responsibility, judgment and a critical eye for sources.

14. Conclusions: use AI, but don’t trust it blindly

AI hallucinations are one of the biggest risks of using AI precisely because they so often look harmless.

They don’t always show up as glaring errors. They show up as fluent sentences, credible sources, confident conclusions and an expert tone.

That’s why an important skill in the AI era isn’t just prompting. It’s also the skill of doubt.

Not cynical doubt that fears everything, but healthy professional skepticism: what is fact, what is interpretation, what is assumption and what needs to be checked?

These are the key takeaways:

1. AI can sound right and still be wrong

Fluency is not the same as truth.

2. Check citations yourself

Ask for sources, but don’t trust them automatically.

3. Use AI for drafting, testing ideas and structuring

Don’t let it settle high-risk questions on its own.

4. Instruct AI to state its uncertainty

A good answer isn’t always a certain answer. Sometimes the best answer is: “This can’t be concluded from the information provided.”

5. Companies need ground rules

Using AI isn’t just an individual skill — it’s part of organizational risk management.

6. Human responsibility doesn’t go away

AI can write, summarize and suggest. But people decide, verify and take responsibility.

Ultimately, understanding hallucinations makes using AI better, not scarier. When we know where AI is strong and where it can stumble, we can use it more wisely.

At its best, AI is an excellent thinking partner. But it can’t yet be a guardian of truth without a human.

The practical summary

If you remember only one thing, remember this:

Ask AI for sources. Open the sources yourself. Check that they really support the claim.

It’s a small effort, but it’s often exactly what separates useful AI use from dangerous overconfidence.

Sources

1. Does ChatGPT tell the truth? — OpenAI Help Center
2. Artificial Intelligence Risk Management Framework — NIST (PDF)
3. Why AI chatbots hallucinate, according to OpenAI researchers — Business Insider
4. LLM09:2025 Misinformation — OWASP Gen AI Security Project
5. AI assistants make widespread errors about the news — Reuters