Large language models (LLMs) can produce plausible but false statements because they learn to predict likely text, not to verify every claim against a source of truth. The practical response is to ground factual answers in reliable evidence, check each claim against that evidence, and let the model say when it cannot answer. These steps reduce risk, but they cannot guarantee correctness.
Why do LLMs hallucinate?
OpenAI defines hallucinations as plausible but false statements generated by language models. During pretraining, a model learns patterns by predicting the next word across large collections of text; those texts generally do not label each statement as true or false. As a result, fluency reflects learned language patterns, not independent fact-checking. A model can sound certain while being wrong.
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Some facts are easier to infer from patterns than others. Repeated language conventions may be learnable from many examples, while a rare or arbitrary detail—such as a particular person’s birthday—may not be recoverable from those patterns. A September 2025 OpenAI paper by Adam Tauman Kalai, Ofir Nachum, Santosh S. Vempala, and Edwin Zhang develops this statistical explanation for why false claims can arise. It is an argument about a source of errors, not proof that every hallucination has the same cause. OpenAI’s explanation and the associated paper describe the account.
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Evaluation can reward guessing
How a model is scored can also affect whether it guesses or admits uncertainty. If an evaluation awards credit for exact answers but not for appropriate abstention, a guess has a chance of earning credit while “I don’t know” earns none. OpenAI’s September 2025 explainer illustrates this with two models on SimpleQA:
#1 Best Overall
| Model | Abstention | Accuracy | Error |
|---|---|---|---|
| GPT-5-thinking-mini | 52% | 22% | 26% |
| o4-mini | 1% | 24% | 75% |
These are results for the named models in that SimpleQA example, not general hallucination rates for LLMs or predictions of how either model will perform in every use. They illustrate why accuracy alone can hide the cost of confident errors. OpenAI’s explainer argues that evaluation should account for errors and abstentions as well as correct answers.
How can you reduce hallucinations in an LLM?
For factual work, treat the model as a system that drafts answers from evidence—not as the evidence itself. A useful workflow moves from finding relevant sources to checking the answer and measuring how the system behaves.
1. Ground answers in reliable, current sources
For current or specialized questions, retrieve relevant documents or search results and provide them to the model as evidence. Retrieval-augmented generation (RAG) is one approach: retrieve relevant external information, then include it in the prompt. This can reduce unsupported claims, but it cannot make a retrieved source accurate or ensure that retrieval found the right material. Check the source’s authority, date, relevance, and coverage before relying on it. Google Cloud’s grounding documentation describes grounding and its claim-checking approach.
2. Check claims individually
A citation that is generally relevant does not necessarily support every detail in an answer. Verify names, dates, quantities, and qualifications against the cited material. Google Cloud’s documented grounding check compares a candidate answer with supplied reference facts, links claims to supporting chunks, and returns an overall support score. Its documentation treats a claim as grounded only when the facts wholly entail it; partial support is insufficient. The API also allows a citation threshold that controls confidence in the cited support. These checks help expose unsupported details, but the underlying references still need to be trustworthy.
3. Allow abstention and clarification
Tell the model to say what it cannot establish from the available sources, or to ask a question when the request is ambiguous. If the evidence does not contain the requested fact, a clear abstention is safer than filling the gap with a plausible-sounding answer. OpenAI’s explainer says it is better to indicate uncertainty or ask for clarification than to provide confident information that may be incorrect.
4. Evaluate the system you actually use
Build a set of prompts representative of your application and compare factual claims with references. Track correct answers, incorrect or unsupported claims, and appropriate abstentions. Include the topics, source types, and edge cases the system is likely to encounter; results from a targeted benchmark do not establish performance on a different domain or prompt set.
OpenAI’s GPT-5 system card reports that, in its tested factuality settings, gpt-5-main had a hallucination rate 26% smaller than GPT-4o, while gpt-5-thinking’s rate was 65% smaller than o3. The card also reports 75% human agreement in validating the factuality grader used in that evaluation. These are publisher-reported, test-specific comparisons—not independent, industry-wide rankings or estimates of the chance that any individual deployed answer is wrong. The system card describes the prompts and methods behind its results. Read the GPT-5 system card for that evaluation’s details.
5. Diagnose source failures as well as answer failures
When an answer is wrong, inspect what the model was given. The cause may be a missing or irrelevant retrieval result, an inaccurate or outdated source, or a generated claim that the source does not support. Distinguishing these cases helps identify whether the workflow needs better retrieval, better sources, or stricter claim checking. It is a practical diagnostic approach, not a universal or measured failure taxonomy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What grounding and benchmark results can—and cannot—tell you
Grounding tools explain how to compare an answer with supplied evidence and identify support for claims; they do not establish a universal reduction in hallucinations across products. Likewise, benchmark and system-card results describe performance under particular test conditions. They should not be presented as typical error rates for all users, tasks, or models. Choose mitigations according to whether the task needs current or specialized facts, how authoritative and fresh the sources are, how well retrieval covers the question, whether claims are tied to citations, whether the model may abstain, and whether evaluation tracks unsupported claims as well as accuracy.
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