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AI hallucinations happen because a language model can generate fluent, plausible text without verifying that its claims are true. Developers can reduce the risk with relevant evidence, appropriate abstention, task-specific evaluation, and ongoing monitoring—but no prompt or retrieval setup guarantees error-free answers. “Lies” is shorthand: a false response does not by itself show that a model intended to deceive.
Why does AI hallucinate?
NIST calls this phenomenon confabulation: generative AI systems can confidently present erroneous or false content, including answers that diverge from the input or contradict something said earlier in the conversation. NIST’s July 2024 Generative AI Profile also notes that the same phenomenon is commonly called hallucination or fabrication.
A language model generates text by predicting likely next tokens based on patterns learned during training and the context it receives. That process can produce accurate statements, but it is not the same as checking a claim against reliable evidence. Fluency, detail, and a confident tone do not show that a statement has been verified. The risk is particularly relevant to open-ended, long answers and questions requiring specialist or contextual knowledge, as NIST explains.
So “AI lies” is a convenient phrase, not a diagnosis of intent. A plausible but false answer is evidence of an inaccurate output; it does not establish that the system knowingly deceived the user.
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Why does AI make things up?
There is no single cause that explains every fabricated answer. The model’s generation process can yield text that fits the prompt statistically without matching the facts. Errors can also be more likely when the needed information is absent from the context, changes over time, or requires expertise the system cannot reliably apply.
Evaluation incentives can matter too. In its September 5, 2025 explainer, OpenAI argues that accuracy-focused scoring can reward a guess while failing to credit an honest “I don’t know.” It recommends treating confident errors as worse than appropriate uncertainty. This is OpenAI’s analysis of one contributor—not proof that scoring incentives are the sole cause of hallucinations.
Fabricated reasoning or citations can make an inaccurate answer seem especially convincing. NIST identifies false confidence and invented citations among the ways confabulations can mislead people, including in high-impact contexts such as healthcare.
How do I stop an LLM from hallucinating?
You cannot reliably eliminate hallucinations with one setting. Choose controls according to the harm an error could cause, then test whether the complete application—not just the underlying model—handles the relevant cases safely.
- Set the risk level. Identify who will rely on the output, what decisions it may affect, how likely an error is, and how serious the consequences could be. Google’s developer guidance recommends understanding application-specific risks and treating safety work as iterative.
- Supply evidence when facts matter. For current or domain-specific questions, retrieve material from sources appropriate to the task and provide it to the model. Google describes Gemini API Search grounding as a feature intended to improve factuality. Treat grounding as a control, not an oracle: retrieved text may be stale, incomplete, irrelevant, or misinterpreted. Check whether sources are relevant and whether each material claim is supported by them.
- Allow abstention or clarification. If evidence is missing, conflicting, or insufficient, let the system ask a follow-up question or say it cannot answer rather than requiring a confident response. Whether abstention is acceptable depends on the task and user experience.
- Evaluate the actual task. Build test cases from the application’s real questions, including ambiguous, out-of-scope, time-sensitive, and low-evidence requests. Score factual support as well as answer completeness; measure correct answers, unsupported or incorrect claims, and appropriate abstentions separately.
- Monitor and revise after release. Collect user feedback and examine observed usage for failures that did not appear in pre-release tests. Update the controls and repeat the evaluation as risks or system behavior change. Google recommends testing, feedback, and usage monitoring as an ongoing cycle.
Set acceptance thresholds for the application’s risk level and review a sample of outputs. The cited guidance does not establish a universal threshold that is safe for every use case.
Does RAG prevent hallucinations?
No. Retrieval-augmented generation (RAG) can give a model relevant source material to work from, which is useful when answers depend on current or specialized information. But retrieving documents does not establish that they are trustworthy, up to date, or relevant to the question, and it does not ensure the model interprets them faithfully. The application still needs to assess source quality and whether the answer’s claims are supported.
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Google describes Search grounding as a feature intended to improve factuality, not a guarantee of truth. More broadly, the available guidance does not establish that RAG—or any single prompt, model size, or setting—can make outputs hallucination-free. NIST frames confabulation as a risk associated with how generative models are designed; Google says hallucinations are very difficult to eliminate, and OpenAI describes them as a persistent challenge even as models improve.
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There is no universal hallucination rate that transfers across models and tasks. Results depend on what counts as an error, the benchmark, the prompts and evidence provided, and how answers are scored. A useful application evaluation makes the definition explicit and distinguishes factual correctness from a system’s willingness to abstain.
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Track errors and abstentions separately
OpenAI’s September 5, 2025 explainer uses SimpleQA results to illustrate why accuracy alone can hide an important trade-off. In that reported example, the models had these separate outcome rates:
| Model and source | Abstention | Accuracy | Error |
|---|---|---|---|
| gpt-5-thinking-mini — OpenAI, 2025 SimpleQA example | 52% | 22% | 26% |
| o4-mini — OpenAI, 2025 SimpleQA example | 1% | 24% | 75% |
These are reported figures for that model pair and benchmark example, not general model error rates or live performance guarantees. OpenAI’s use of “hallucination” for errors here is tied to the short-answer benchmark. The comparison illustrates why a test should not reward a system merely for answering more often: count appropriate abstentions as a distinct outcome.
Read vendor benchmarks within their scope
OpenAI’s GPT-5 System Card reports results from its own production-representative evaluation: GPT-5 main had a 26% smaller hallucination rate than GPT-4o, while GPT-5 thinking had a 65% smaller rate than o3. The card also reports 44% fewer responses with at least one major error for GPT-5 main and 78% fewer for GPT-5 thinking versus o3. These are vendor-reported, model-specific comparisons, not independent or universal findings; the evaluation used LLM-based grading, for which the card reported 75% agreement with human factuality judgments.
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Test the whole application
For your own system, inspect whether claims are supported by the evidence available at response time, whether the system flags uncertainty appropriately, and whether it handles missing or contradictory material safely. Include the retrieval and application logic in evaluation, because a model-only score cannot show whether your sources are fresh or whether your product presents an unsupported claim as established fact. Review examples of failures as well as aggregate scores, then use them to refine tests and controls.
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