AI agents can give confident, plausible answers that are false because a language model generates text from learned patterns, not from a complete, verified record of facts. Retrieval-augmented generation (RAG) can give a model relevant external material to use while answering, especially for specialized or changing information. But RAG is not a truth filter: the system can retrieve poor evidence or misrepresent good evidence, so its answers still need evaluation.
What does it mean when an AI agent hallucinates?
A hallucination is a plausible but false statement generated by a language model. The term describes the answer’s relationship to the facts, not whether it sounds convincing: fluent wording and apparent confidence are not evidence of correctness. OpenAI uses this definition in its September 2025 explanation of language-model hallucinations.
An AI agent is typically more than a model alone: it may combine a model with instructions, tools, and access to information sources. Those additions can help it perform tasks, but they do not make every answer verified. The model still has to produce an answer, and the surrounding system has to supply and handle evidence appropriately.
Why do language models make things up?
Training teaches prediction, not a complete fact-checking database
During pretraining, a language model learns to predict likely next words from examples of text. That can produce useful general knowledge and fluent explanations, but it does not give the model a complete table that labels every possible claim as true or false. Some details—such as an obscure person’s birthday—may not be reliably recoverable from patterns in the model’s learned text.
Free tools Windows power users keep installed
One-click scans. No signup required.
#1 Best Overall
As a result, when a question asks for information the model does not know reliably, it may still generate a plausible continuation. The answer can resemble a fact without being grounded in a source that establishes it.
Evaluation can reward guessing
OpenAI’s 2025 explanation also argues that evaluation incentives can encourage overconfident answers. If a benchmark rewards correct answers but penalizes leaving a question unanswered, a model may gain by guessing when uncertain. OpenAI argues for evaluations that penalize confident errors more heavily and reward appropriate uncertainty; this is its proposed direction, not proof that every deployed model is trained with identical incentives.
To illustrate the trade-off, OpenAI reported results on its SimpleQA evaluation in September 2025: gpt-5-thinking-mini had a 52% abstention rate, 22% accuracy rate, and 26% error rate, while OpenAI o4-mini had a 1% abstention rate, 24% accuracy rate, and 75% error rate. These are vendor-reported results for those named systems on that evaluation, not estimates of hallucination rates across AI agents or real-world deployments.
How retrieval-augmented generation works
Retrieval-augmented generation adds selected external material to the model’s prompt before it generates an answer. OpenAI’s API guide describes RAG as retrieving content to augment an LLM’s prompt before generating an answer. In a typical flow:
Recommended Free Tools
- Receive a question. The system gets the user’s request.
- Search a collection. A retrieval component looks for passages relevant to that request in an available document collection.
- Add context to the prompt. The system supplies selected passages to the language model along with the question and instructions.
- Generate an answer. The model uses the supplied context to formulate a response. A well-designed system can also make the supporting material visible for review.
This gives the model an opportunity to use information that is specialized, external to its learned parameters, or more current than that knowledge. The collection can be maintained separately from the model, so its contents can be updated without retraining the model. RAG is especially useful when the task depends on a known, maintained body of material and the system can show which sources support its claims.
Where RAG can fail
RAG introduces an evidence path; it does not guarantee a correct answer. OpenAI’s accuracy guidance identifies two broad failure points:
- Retrieval failure: The system may find the wrong passages, miss the relevant material, or supply so much irrelevant context that useful evidence is obscured.
- Generation failure: Even when the model receives relevant evidence, it may misread it, draw an unsupported conclusion, or answer in a way that conflicts with the source.
These are separate problems and need separate checks. A response that cites a document is not automatically faithful to it; a retrieved passage may be irrelevant, incomplete, outdated, or insufficient to support the strength of the claim. Instructions to use the context can help, but the outcome must still be evaluated.
Security is another system-level concern. NIST’s draft account of the NCCoE chatbot discusses prompt injection, hallucinations, data exposure, and unauthorized access alongside safeguards used in its prototype, including local deployment, access controls, and validation filters. That document describes a point-in-time internal prototype and explicitly is not implementation guidance; its choices should not be treated as a universal security checklist.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
A bounded example: NIST’s NCCoE chatbot
NIST’s draft report, IR 8579, Developing the NCCoE Chatbot: Technical and Security Learnings from the Initial Implementation, describes an internal chatbot intended to help staff discover and summarize cybersecurity guidance from NCCoE publications. It illustrates why a maintained, domain-specific corpus can be a practical grounding source: the task concerns a defined set of guidance rather than every fact on the internet.
The report is about an initial implementation, not evidence that RAG makes answers universally accurate or that the prototype’s design will suit other organizations. It is best read as a concrete example of both the potential use case and the risks that have to be managed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an agentic RAG system
Compare an ungrounded agent with a RAG-enabled one—or compare two RAG pipelines—on the same task and under comparable evidence conditions. Do not assume that adding retrieval makes a system categorically more accurate. Check the pipeline and the answer across distinct dimensions:
- Retrieval relevance and focus: Did the system find the right passages, and did it avoid burying them in noise?
- Faithfulness: Does each substantive claim follow from the evidence actually supplied?
- Completeness: Does the response preserve important qualifications and context, rather than selectively using only part of a source?
- Evidence sufficiency: Is the material strong enough to support the claim being made, or does the answer overstate what it establishes?
- Traceability: Can a reviewer see what the agent found and how that evidence supports its conclusion or action?
- Uncertainty behavior: When evidence is absent, conflicting, or ambiguous, does the system say so, abstain, or ask a clarifying question instead of fabricating an answer?
RAGAS, a research framework described by Shahul Es and co-authors, separates retrieval relevance, faithful use of context, and answer-generation quality. NIST’s agent-evaluation probe work describes checks for faithfulness, completeness, and sufficiency against curated reference documents, as well as structured audit trails. These efforts support evaluating multiple parts of the system; neither establishes one universal score that proves an agent is safe or free of hallucinations. NIST describes its probe work as an evolving evaluation effort, not a settled standard.
When is RAG the right intervention?
RAG is a strong candidate when answers depend on a known collection of documents—such as internal guidance—or information that changes more often than a model can be expected to have learned it. It also helps make evidence inspectable when the system preserves links between retrieved material and generated claims.
It is less useful as a blanket fix for every wrong answer. If the system retrieves unreliable material, lacks the needed evidence, or cannot reliably follow evidence, adding retrieval alone will not resolve the underlying issue. OpenAI’s guidance treats retrieval tuning and model instructions as ways to address RAG-specific problems, while fine-tuning is a separate approach for some learned-task problems. Choose based on the failure observed, then test the resulting system on the task it is meant to perform.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
