AI agents hallucinate because language models can generate convincing text without verifying that each claim is true. Retrieval-augmented generation (RAG) can give an agent relevant external evidence at answer time, but it is not a truth filter: retrieval can fail, and a model can misread or misuse good evidence. The practical goal is not to assume RAG makes answers correct, but to test whether an agent finds, uses, and faithfully reports evidence.
Why an AI agent can sound certain and still be wrong
OpenAI defines hallucinations as “plausible but false statements generated by language models.” Fluency and confidence are features of how an answer is expressed; neither proves that its claims are accurate.
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Language models learn to predict text, not to consult a complete fact table
In its September 5, 2025 explainer, Why language models hallucinate, OpenAI describes pretraining as learning to predict the next word from examples of text. That process can produce remarkably useful answers, but it does not give a model a complete record of which statements are true or false. A rare or arbitrary detail—such as a person’s birthday—may not be reliably recoverable from patterns in training examples.
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Some evaluations can reward guessing
If an evaluation rewards correct answers but penalizes leaving a question unanswered, guessing may score better than admitting uncertainty. OpenAI’s 2025 paper and explainer argue for evaluation that penalizes confident errors more heavily and rewards appropriate uncertainty. This describes a potential incentive problem, not evidence that every deployed model has been trained or evaluated the same way.
#1 Best Overall
OpenAI’s September 2025 explainer reported these results on its SimpleQA comparison:
| System | Abstention rate | Accuracy rate | Error rate |
|---|---|---|---|
| gpt-5-thinking-mini | 52% | 22% | 26% |
| OpenAI o4-mini | 1% | 24% | 75% |
These are vendor-reported results for the two named systems on that evaluation. They are not estimates of how often AI agents hallucinate in general or in production.
What retrieval-augmented generation does
OpenAI’s API guide defines RAG as “the process of Retrieving content to Augment your LLM’s prompt before Generating an answer.” In plain terms, the system searches for useful material and gives selected passages to the language model as context before it answers.
- Receive a question. The system determines what information it needs to answer.
- Retrieve passages. It searches a document collection, such as product documentation, policies, or a curated set of reference material.
- Add context to the prompt. The selected passages are supplied to the model alongside the question.
- Generate an answer. The model uses the question and retrieved context to formulate a response. A well-designed system can also expose the sources behind its claims.
Because the material can be maintained separately from the model’s learned parameters, RAG can help with specialized information or updates not reliably available in those parameters. It is most useful when the task depends on a known, maintained collection and the system can show which passages support its answer. A literature overview by Yunfan Gao and coauthors, posted to arXiv on December 18, 2023, surveys RAG approaches; it is an overview of a developing field, not a guarantee that any particular implementation will work.
Where RAG systems can still fail
RAG introduces an evidence path, but that path has two distinct failure points, as OpenAI’s API guide notes.
Retrieval can return poor evidence
A search may miss the relevant passage, select a passage about a similar but different topic, or return so much irrelevant context that useful information is obscured. If the evidence supplied to the model is wrong or unfocused, the generated answer may inherit those problems.
The model can use good evidence badly
Even when retrieval finds the right material, the model may misinterpret it, overlook a qualification, make an unsupported inference, or answer with a claim the passage does not establish. Showing a citation is useful only if the cited material actually supports the claim.
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Security and access matter too
NIST’s NCCoE draft, IR 8579, Developing the NCCoE Chatbot: Technical and Security Learnings from the Initial Implementation, describes an internal chatbot prototype for finding and summarizing cybersecurity guidance in NCCoE publications. The draft discusses risks including prompt injection, hallucinations, data exposure, and unauthorized access, as well as safeguards used in that point-in-time prototype. It explicitly is not implementation guidance, so its design choices should not be treated as a universal checklist.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an agent that uses RAG
Test the retrieval and answer stages separately, then assess whether the whole system behaves appropriately when evidence is weak. NIST’s agent-evaluation work and the RAGAS framework by Shahul Es and coauthors describe dimensions that can help organize such testing; neither establishes a single score that proves a system is safe or hallucination-free.
- Retrieval relevance and focus: Did the system find the passages needed for the question, without burying them in noise?
- Faithfulness: Does each factual claim follow from its supporting passage, rather than merely appearing beside a relevant citation?
- Completeness: Does the answer preserve important context and qualifications, or leave out information that changes the meaning?
- Evidence sufficiency: Is the retrieved material strong enough to support the level of certainty in the claim?
- Traceability: Can a reviewer see what the agent found and how that evidence supports its conclusions? NIST’s May 2026 agent-evaluation project describes this as moving beyond “the AI said so” to showing what it found, where it found it, and how the evidence supports the conclusion. The project is evolving research, not a settled standard.
- Uncertainty behavior: When evidence is missing, conflicting, or ambiguous, does the agent say so, abstain, or ask for clarification instead of inventing an answer?
For a meaningful comparison—such as an ungrounded agent versus a RAG-enabled one—hold the task and evidence conditions constant. Compare relevance, faithfulness, completeness, sufficiency, traceability, and appropriate abstention. Without a task-specific evaluation, RAG should not be described as categorically more accurate.
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