AI models make things up because they generate likely text, not because they automatically check every claim against reality. When their information is missing or uncertain, they can still produce a fluent answer—and may fail to signal that it is a guess. “Hallucination” is the term for this kind of plausible but false AI output, not a claim that a model literally perceives something.
Why can a model sound certain when it is wrong?
A language model generates text one piece at a time, based on patterns learned during training and the conversation so far. That process can produce a convincing continuation without verifying whether each detail is true. Fluency is therefore not evidence of accuracy.
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One explanation focuses on incentives. In its September 5, 2025 explainer, OpenAI argues: “Our new research paper argues that language models hallucinate because standard training and evaluation procedures reward guessing over acknowledging uncertainty.” In a simple accuracy-only test, a guessed answer may earn credit if it happens to be right, while abstaining may earn none. That can favor a guess even when the model has weak grounds for one. This is OpenAI’s explanation of an important contributor, not a settled account of every model error.
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What kinds of failures lead to made-up details?
There is no single cause that explains every false answer. Google researchers distinguish between errors associated with missing knowledge and errors made despite relevant knowledge being available to the model. The distinction matters: a system may need better evidence in one case, but better uncertainty handling in another.
| Failure type | What happens | Why it matters |
|---|---|---|
| Knowledge-related error (HK−) | The model lacks relevant knowledge or has incomplete or unreliable information. | It may fill gaps with a plausible continuation rather than recognize that the answer is unsupported. |
| Error despite relevant knowledge (HK+) | Relevant information is available to the model, but its answer is still wrong. | Providing more information alone may not solve the problem; the model may fail to use or express what it knows appropriately. |
| High-certainty error | The model gives incorrect information without suitable qualification. | The confidence of the wording can make an error harder for a reader to spot. |
These categories, set out in Google Research’s 2024 analysis, help separate a lack of evidence from a failure to handle evidence or uncertainty. They are ways to analyze errors, not proof that every hallucination fits neatly into only one category.
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Can a model mistake recognizing a name for knowing the answer?
Anthropic’s 2025 interpretability study of Claude describes a possible model-specific mechanism. In the experiments, a default refusal mechanism could be suppressed by a feature associated with recognizing a known entity. If recognition of a person or place is mistaken for knowing the requested fact, the model may continue with a plausible but untrue answer.
Anthropic summarizes the broader training pressure this way: “At a basic level, language model training incentivizes hallucination: models are always supposed to give a guess for the next word.” The circuit findings concern Claude and the prompts and methods studied; they do not establish that all AI systems use the same mechanism. Anthropic also cautions that its interpretability method captures only part of the model’s computation and may include artifacts.
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Does web search or retrieval prevent hallucinations?
Search and retrieval can give a model external evidence to use, which may reduce errors caused by missing information. They are not guarantees. A search may fail to find a relevant source, retrieve weak or misleading material, or be summarized incorrectly. And some mistakes—such as a miscalculation—can occur even when relevant information is present.
- Retrieval supplies evidence: it can help fill gaps, but the answer still needs to reflect the evidence accurately.
- Abstention avoids unsupported answers: a system can decline to answer when it lacks adequate grounds, though excessive abstention can make it less useful.
- Uncertainty signaling qualifies an answer: the model can indicate what is unclear instead of presenting a weak inference as settled fact.
- Evaluation can reward calibration: scoring systems can give credit for appropriate uncertainty rather than rewarding only a correct guess.
Google Research’s 2026 position paper proposes uncertainty expression as a path beyond a simple answer-or-abstain choice. Its authors write: “If we understand hallucinations as confident errors — incorrect information delivered without appropriate qualification — a third path emerges beyond the answer-or-abstain dichotomy: expressing uncertainty.” This is a proposed framing and research direction, not a claim that uncertainty signaling has eliminated false answers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you check a confident AI answer?
Treat a polished answer as a lead, not proof—especially when the claim is current, consequential, or difficult to reverse. For important decisions, inspect the cited primary material and verify the key facts independently. A citation can help you check an answer, but its presence alone does not establish that the answer accurately represents its source.
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Quick Recap
- Look for a source that directly supports the specific claim, not merely one that discusses the same topic.
- Check dates, names, figures, and qualifications against the source itself.
- If the answer gives no verifiable basis for a high-stakes claim, do not treat confident wording as a substitute for evidence.
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