A chatbot can deliver a polished answer and still invent the facts that make AI-generated information seem reliable. For a user, that gap matters when prompts guide research, business choices, or decisions in the wider world.
This article explains why generative systems invent details, how errors appear in text and images, and what you can do to judge and reduce risk. A clear writing style can hide a weak claim, so check important facts before you rely on them.
Key Takeaways
- Fluent answers can include false or unsupported claims.
- Errors can affect text, images, audio, and video.
- Accuracy depends on the task and the way it is tested.
- Trusted documents can help ground an AI response.
- Human review matters most when an answer could affect a decision.
Treat any AI-generated claim that could change a decision as a draft until it has been checked.
AI Hallucinations: Why AI Makes Up Information at All
A model can produce a plausible sentence without having checked whether its claim is true. A hallucination is false, misleading, or unsupported information presented as fact.
Large language models (LLMs) predict the next likely token, or piece of text, from context. They aim to produce plausible language, not independently verify each claim. Retrieval-augmented generation (RAG) retrieves relevant material from a trusted knowledge base to ground an AI-generated answer.
That process helps explain why a response can sound smooth while its information is wrong. A model learns patterns in language, then uses those patterns during generation.
A convincing tone is a feature of the output, not evidence that the claim is accurate.
The same broad issue can affect other generative systems. Image, audio, and video generators build outputs from learned patterns in pixels or waveforms.
A text model may create a statement that fits its context but lacks support. An image system may add a detail that fits its learned patterns but was never requested.
The University of Illinois library guide explains how generative systems can present nonexistent content as fact across different media.
A response’s confidence tells you how it is phrased, not whether its information has been checked.
Why Does a Chatbot Fill Gaps With Made-Up Details?
A model is most likely to fill in details when its learned patterns do not provide a reliable answer. Gaps in its knowledge can leave it leaning on patterns that only seem to fit.
Incomplete, outdated, biased, or noisy training data can teach patterns that do not reflect the world well. A model may repeat errors found in its data or treat a narrow view as common.
Training data leaves gaps the model must fill
Overfitting can make a model rely too much on details from training examples instead of learning a broader rule. Weak grounding also leaves the model without a trusted source to guide its answer.
For example, an image classifier might learn that wolves appear in snowy scenes. It could then label a dog as a wolf because of snow, not the animal itself.
This is one of the flaws of AI: a system can learn the wrong clue and still produce a neat result. Its word choice may sound certain even when the pattern does not fit.
Prompts and incentives can reward confident guesses
A vague prompt can leave key facts open, while pressure to give a complete answer can encourage a guess. A chatbot may also agree with a false premise instead of correcting it.
Prompt injection places new instructions in a model’s context and tries to override its original setup. This instruction attack can manipulate output, but it is different from a simple factual mistake.
When a prompt lacks detail, a model may fill in missing pieces rather than show uncertainty. Treat a rare or poorly specified question as a higher-risk request, especially when the model has no trusted material to consult.
What Do AI Hallucinations Look Like in Real Outputs?
A hallucination may be a made-up citation, a detail absent from a supplied document, or an image that misrepresents what was requested. Look for the unsupported detail, not just strange wording.
Check whether a claim can be verified and supported. A claim does not become reliable just because it sounds familiar, unusual, or neatly written.
Common examples include:
- An invented citation or statistic that looks precise.
- A response that adds facts absent from its supplied document.
- Contradictory claims made during one conversation.
- An image with an extra or distorted object.
A Google AI Overview once suggested adding nontoxic glue to pizza sauce to make cheese stickier. The suggestion drew on a 2013 Reddit joke.
Biases can shape an output without making every biased response a hallucination. A biased answer may reflect an unfair pattern, while a hallucination presents unsupported information as fact.
For example, a chatbot may misread a supplied text or invent details that sound as if they came from it. An image generator can also create inaccurate image details that are hard to notice at a glance.
The University of Maryland Libraries explain that AI can invent facts, citations, or details when it misreads a prompt. Compare consumer tools in this AI chatbot app comparison, but judge their claims against the original material.
Open the cited source to see if it supports the specific claim, as well as the surrounding topic. Check the original document or image prompt before deciding whether a mismatch is a factual error, a context error, or a visual one.
What Can a False AI Answer Cost?
A false answer becomes more than an inconvenience when a person or an automated workflow acts on it. The harm can spread from one claim into legal, customer, or safety decisions.
An agentic AI workflow can turn an unsupported statement into action when later systems treat it as verified. The table shows how that risk can arise in real cases and a hypothetical workflow.
| Case or scenario | False claim | Consequence |
|---|---|---|
| Mata v. Avianca, 2023 | ChatGPT-generated judicial decisions were real | Lawyers were sanctioned after submitting them |
| Air Canada, 2024 | Its chatbot gave correct bereavement discount information | A Canadian tribunal held Air Canada liable |
| Hypothetical cloud-storage workflow | A bucket had no read activity for 90 days | Later agents delete records under legal hold |
In 2025, the AI Incident Database recorded 362 incidents, up from 233 in 2024. These incidents show why a false output can matter beyond its first reader.
Common risk areas include:
- Legal liability when false claims enter formal work.
- Customer trust when a service gives wrong information.
- Health or safety decisions based on inaccurate answers.
- Cascading agent actions that rely on earlier claims.
Businesses using AI for client work should review claims, names, and numbers before sharing AI-assisted services. Require human approval before an AI-driven action can delete, publish, or commit anything consequential.
How Accurate Are AI Models on Factual Questions?
A model’s accuracy is not one fixed number, because results change with the benchmark and task. Each test measures a slice of performance, not every use.
Accuracy and support from cited sources are different measures. A response may give the right answer while its citations fail to support it.
| Evaluation | What it tests | Reported result |
|---|---|---|
| 2026 Stanford AI Index benchmark | Factual accuracy across 26 top models | Hallucination rates ranged from 22% to 94%; GPT-4o fell from 98.2% to 64.4% accuracy, while DeepSeek R1 fell from over 90% to 14.4% |
| Oumi Google AI Overview assessment | Answers and cited-source support using SimpleQA, with just over 4,000 factual queries | About 91% had the correct answer; 39% were correct and fully supported; 67% of claims had cited-source support |
| AA-Omniscience | Factual recall and knowledge calibration across 6,000 questions, 42 topics, and six domains | Scores range from -100 to 100; zero means correct answers as often as incorrect ones |
Oumi compared Gemini 2-powered Overviews in late 2025 with Gemini 3-powered Overviews in early 2026. Gemini 3 showed higher accuracy alongside a higher rate of unsupported claims.
AA-Omniscience rewards abstaining when uncertain and penalizes hallucinations. Its score measures knowledge and calibration, not a universal level of intelligence.
The Oumi assessment results show why a correct answer and a well-supported answer should not be treated as the same outcome. Test design shapes what a score can tell you.
A result on one set of questions cannot promise the same performance on a different task. Compare systems only on evaluations that resemble the decisions and information your own use case requires.
Do Reasoning Models Make Fewer Mistakes?
More steps do not guarantee a more accurate answer, especially when a problem exceeds the model’s reliable range. Reasoning helps in some cases, but its effect depends on task difficulty.
Standard models can do better on simple puzzles, while reasoning models can gain an edge at medium complexity. A 2025 Stanford study found that both types failed sharply on highly complex puzzles.
The study also found that reasoning effort rose with problem complexity up to a point. It then declined, even when the models had enough tokens available.
That result challenges the idea that longer responses always show stronger capabilities. A detailed answer can repeat an incorrect premise through several steps instead of fixing it.
The Stanford study on reasoning limits tested controllable puzzle tasks, so its findings do not set a universal rate for factual errors.
Prompt framing can change responses, too. The 2026 Stanford AI Index found that models handled false statements better when framed as another person’s belief than as the user’s belief.
Performance collapsed when the false statement appeared to be the user’s belief. This prompt effect can make a chatbot agree with a mistaken claim, even when it might challenge the same claim in another form.
When revising AI-written pages, use AI for SEO as a drafting aid, then check each factual claim against reliable material. For a difficult question, verify each key premise instead of treating a detailed explanation as proof.
How Can People and Organizations Reduce Hallucination Risk?
Grounding an answer in an approved source gives a system something concrete to check instead of relying only on learned patterns. Trusted material narrows the task, though it cannot ensure every statement is right.
Retrieval-augmented generation (RAG) connects a model to a trusted knowledge base and retrieves relevant material for an answer. The retrieved passage must still support each claim.
Ground answers in trusted material
Keep the document collection focused, current, and relevant to the questions people ask. Check whether each retrieved passage supports the claim, rather than trusting a nearby citation.
MEGA-RAG reduced hallucination rates by over 40% compared with PubMedBERT, PubMedGPT, a standalone LLM, and an LLM with standard RAG. Its scores were 0.7913 accuracy, 0.7541 precision, 0.8304 recall, and 0.7904 F1.
Those results describe one evaluation, not a promise for other systems. When you compare ChatGPT apps and connections, check what connected accounts can read or change and what approval steps apply.
Constrain, test, and review outputs
Set clear limits on prompts, ask for an abstention when support is missing, and test against verified examples. Use adversarial tests to see whether prompt injection can change a response or trigger an unsafe action.
- Test before launch and after major changes.
- Monitor outputs against representative, verified examples.
- Use access controls to limit what each system can read or change.
- Set human review for high-impact outputs and downstream actions.
- Assign an owner who tracks risks across the workflow.
NIST’s voluntary Generative Artificial Intelligence Profile, published in July 2024, accompanies its AI Risk Management Framework. It recommends governing, mapping, measuring, and managing risk across the AI lifecycle.
OWASP’s Top 10 for Agentic Applications 2026 was published on December 9, 2025. More than 100 industry experts, researchers, and practitioners helped develop its guidance.
Human approval belongs before an AI output can change a record or reach a customer.
These controls can limit harm from manipulated inputs and later system actions, but they cannot remove every error. Set a named owner and review threshold for every AI workflow that can affect a customer, record, or regulated decision.
Conclusion
Useful AI starts with a clear boundary around what you are willing to trust it to do. It can help you draft, sort ideas, and explore a topic, while factual claims still need judgment.
Match your review to the possible effect of an error. A low-stakes draft needs less scrutiny than a claim that could change a customer’s choice or a formal record.
Choose one upcoming AI-assisted task, decide what evidence would make its answer usable, and identify who has authority to approve it.
FAQ
Can AI hallucinations be eliminated completely?
No, current safeguards cannot guarantee zero errors. Trusted source material, clear prompts, and tests can reduce how often unsupported answers appear. Review can also limit the harm when a mistake slips through. Set the level of review based on the stakes, and do not let a high-impact answer pass without a person checking it.
Does adding a citation make an AI answer trustworthy?
No, a citation alone does not prove that an answer is correct. Open the cited page and confirm that it exists, comes from a suitable source, and supports the exact claim. A page about the same topic may still fail to back up a specific number, date, or rule.
Is a hallucination the same as an ordinary AI mistake?
No, a hallucination presents false, fabricated, or unsupported information as fact. An ordinary mistake can be an inaccurate or suboptimal result without invented content. For example, a tool might sort items in the wrong order, while an invented quote claims someone said words they never used.
Can retrieval-augmented generation still produce a wrong answer?
Yes, retrieval-augmented generation can still produce a wrong answer. A search step may return irrelevant or incomplete material, and the model may misstate a relevant document. A citation can help you inspect the basis for an answer, but it does not replace checking whether the text supports each claim.
Why might an AI agree with a false statement in a prompt?
Prompt framing can affect whether a model challenges or accepts a false statement. A chatbot may respond differently when the claim sounds like the user’s own belief. Restate the issue in neutral terms, ask what evidence supports it, and check the premise rather than treating agreement as confirmation.
How often should an organization test an AI system for hallucinations?
Test an AI system before deployment, after material changes, and on an ongoing basis. Use representative examples with verified answers, then track whether performance shifts across common tasks. Changes to prompts, connected data, or system access can affect results, so include those changes in review and monitoring plans.
