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Large language models (LLMs) can produce answers that sound convincing but are false. The practical fix is not a magic prompt: check important claims against reliable sources, and, when building an AI system, ground answers in relevant evidence and test how often it gets facts right or appropriately declines to answer.
What does it mean when an LLM hallucinates?
OpenAI defines hallucinations as “plausible but false statements generated by language models.” A fabricated quotation, an incorrect date, or a nonexistent source can all fit that description. The term describes an output, not a mysterious state of mind: fluent phrasing and specific details do not prove that a claim is true. OpenAI explains the term and the challenge.
Why do language models make things up?
They generate likely text, not guaranteed facts
A useful analogy is autocomplete. During pretraining, a model learns patterns in text and generates likely continuations. That helps explain how it can produce a plausible answer without a dependable fact-checking step. The analogy is only a simplification: a deployed LLM is not merely a basic autocomplete feature, and the analogy does not explain every cause of every error.
Some tests reward guessing
If an evaluation gives credit for a correct answer but not for a well-judged “I don’t know,” guessing can be more rewarding than abstaining. OpenAI argues that many accuracy-focused evaluations create this incentive. That does not mean every model always guesses, but it helps explain why confident answers can appear even when evidence is weak. OpenAI’s explanation of hallucinations discusses this evaluation problem.
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Can hallucinations actually be stopped?
No prompt, retrieval system, or evaluation method can guarantee that an LLM will never produce a false claim. Mitigations can reduce risk, but OpenAI describes hallucinations as a continuing challenge. Abstaining when evidence is insufficient is often better than inventing an answer; refusing too often, however, makes a system less useful. The goal is a sensible balance between correctness, appropriate uncertainty, and usefulness—not a “hallucination-free” label.
How to check an answer as a reader
- Ask for sources when a claim matters. Treat model-generated citations as leads, then open the cited material and check that it exists and supports the claim.
- Request clear separation between evidence and inference. You can ask the model to identify what it cannot establish and distinguish sourced facts from its interpretation. This may make uncertainty easier to see; it does not guarantee that the model is well calibrated.
- Verify the details most likely to matter. Check names, dates, quotations, calculations, current information, and consequential advice against authoritative sources. For high-stakes decisions, consult an appropriate primary source or qualified professional.
- Do not treat confidence as proof. A polished, decisive answer is still an answer to verify when accuracy matters.
How developers can reduce errors in AI applications
Ground answers in relevant evidence
Retrieval-augmented generation (RAG) supplies a model with external context to use when answering. It helps only if the retrieved material is suitable and the model uses it correctly. Retrieval may return the wrong material or bury useful evidence in irrelevant context; even with good context, the model can still answer incorrectly. OpenAI’s API guide puts the task plainly: “For our RAG to work, we need to give the right context to the model, and then assess whether the model is answering correctly.” It recommends tuning retrieval toward relevant, low-noise results and assessing how well answers use that context. The guide names LlamaIndex and LangChain as examples of libraries with RAG-tuning approaches, not as guarantees of accuracy. Read the OpenAI API guide to optimizing LLM accuracy.
Evaluate the trade-offs, not just raw accuracy
Test with prompts, evidence, and tasks that resemble the application’s real use. Measure factual correctness and unsupported or false claims, while also tracking abstention or refusal and whether users receive useful answers. Otherwise, a system that declines nearly everything could look safe on error counts while failing its purpose. For grounded applications, inspect retrieval quality and noise as well as the final response.
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- Does the system abstain when evidence is insufficient?
- How often does it answer questions it can reasonably answer?
- Does retrieval provide relevant evidence without excessive noise?
- Do test cases reflect current, ambiguous, and consequential questions from the intended use?
Compare systems using the same task distribution and evidence. OpenAI’s 2025 evaluation report illustrates that hallucination and refusal behavior can vary across models and test settings; its results come from a specific pilot exercise with specific prompts and model versions, not a general rate for everyday use or a universal ranking. See OpenAI’s 2025 hallucination evaluation report.
What OpenAI’s birthday-question example shows—and does not show
In an example in its hallucination article, OpenAI reports that one compared model abstained 52% of the time, answered accurately 22% of the time, and answered incorrectly 26% of the time. The other abstained 1% of the time, answered accurately 24% of the time, and answered incorrectly 75% of the time. These figures belong to that birthday-question example; they are not general rates for all models, tasks, or real-world use. They illustrate why accuracy and willingness to abstain should be considered together: answering more often does not necessarily mean answering more reliably. OpenAI’s article provides the example and context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are “confessions” in AI monitoring?
OpenAI has described “confessions” as an early proof-of-concept approach for models to report instruction violations or shortcuts. It is an experimental monitoring and training idea, not an end-user technique that stops hallucinations, and OpenAI does not present it as a complete solution. Read OpenAI’s description of confessions.
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