AI chatbots can produce fluent, confident answers that are false. That happens because generating plausible text is not the same as checking facts, and some evaluation methods can reward a guess over an admission of uncertainty. You can reduce the risk by asking bounded questions, checking cited evidence yourself, and relying on qualified sources for consequential decisions—but no prompt or workflow guarantees a correct answer.
What does it mean when a chatbot “hallucinates”?
A hallucination is a plausible-sounding but false statement generated by a language model. OpenAI defines the term this way in its September 5, 2025 explanation. NIST uses “confabulation” for generated content confidently presented despite being erroneous or false; it notes that hallucination and fabrication are also used for the phenomenon in its July 26, 2024 Generative AI Profile.
As an Amazon Associate I earn from qualifying purchases.
The wording can sound certain even when the answer is not. Fluency, detail, a step-by-step explanation, or a list of citations does not establish that a claim is true.
Why do AI chatbots make things up?
They generate likely text, not verified facts
Language models learn statistical patterns in text and use them to generate likely continuations. This can produce coherent, useful answers, but it is not an automatic fact-checking process. A model may produce inaccurate or internally inconsistent information, particularly in open-ended, long-form answers or questions requiring specialist knowledge. NIST describes these as risks of generative AI, not simply a matter of an occasional mysterious glitch.
#1 Best Overall
Patterns in training examples cannot reliably reveal every rare or arbitrary fact. For example, unknown details about a particular person cannot be inferred with certainty just because the model can write a plausible-sounding biography. When the information needed to answer is absent or unclear, a confident completion can still be wrong.
Some evaluations can reward guessing
OpenAI’s 2025 analysis identifies an incentive problem: if a test rewards correct answers but penalizes abstaining, a model can score better by guessing than by saying it does not know. A lucky guess may count as correct, while a cautious non-answer may count as wrong. This is one documented mechanism for confident mistakes, not a complete explanation of every false output.
Rank #2
OpenAI’s SimpleQA example illustrates why accuracy alone can obscure the tradeoff. In that specific example, gpt-5-thinking-mini had 22% accuracy, a 26% error rate, and a 52% abstention rate; o4-mini had 24% accuracy, a 75% error rate, and a 1% abstention rate. These are results for the named models on that test, not general error rates for ChatGPT or AI chatbots. Accuracy alone makes o4-mini look slightly better, while the error and abstention figures show a very different balance between wrong answers and refusals to guess.
Free tools Windows power users keep installed
One-click scans. No signup required.
Explanations and citations can also be wrong
A citation is a claim about evidence, not proof that the evidence exists or supports the answer. NIST warns that generated citations and reasoning can themselves be confabulated. A real source may also be irrelevant, out of date, or misread. Open the cited material and check whether it directly supports the claim before relying on it.
How to reduce the risk of relying on a false answer
- Make the question specific. Include relevant context, a timeframe, and the kind of answer you need. If a question could mean more than one thing, ask the chatbot to identify the ambiguity or ask you to clarify.
- Ask it to separate facts from uncertainty. You can say, “If you do not know, say so; do not guess.” OpenAI’s guidance says uncertainty or a clarification request is preferable to confident information that may be incorrect. This wording signals your preference; it cannot guarantee the chatbot will abstain or be accurate.
- Ask for evidence, then inspect it. Request primary sources, dates, and the exact passage or data behind important claims. Open each source yourself, confirm it exists, and make sure it supports the specific claim—not just the general topic.
- Check changing facts against a current source. Schedules, policies, prices, laws, and recent events can change. Use a current source where available and verify it directly. OpenAI’s help page describes search and deep research as ways ChatGPT can access current web sources, but availability depends on the product. Browsing does not by itself make an answer true.
- Corroborate important claims independently. Look for a second reliable source, preferably one independent of the first. If reputable sources disagree, note the disagreement and their dates instead of presenting a forced, confident answer.
- Verify calculations, quotations, and references. Recalculate with an appropriate tool; compare quotations word for word with the original; and confirm that referenced documents contain the details attributed to them.
- Use qualified review for high-stakes decisions. For health, legal, financial, safety, or similarly consequential matters, check authoritative records and consult a qualified professional or other appropriate expert. A false answer can matter most when someone acts on it.
How organizations can manage chatbot errors
For developers and organizations, reducing exposure calls for more than instructing a chatbot to be careful. NIST’s Generative AI Profile treats confabulation as a risk to identify and manage across the system lifecycle, with controls suited to the use case. OpenAI’s analysis argues that evaluations should reward appropriate uncertainty and penalize confident errors rather than relying only on accuracy scores.
Depending on the application, practical controls can include grounding answers in trusted material, evaluating both factual claims and abstentions, monitoring performance, and requiring human review for consequential decisions. These are risk-management measures, not a universal architecture or a guarantee that errors will disappear.
Rank #4
How to interpret chatbot accuracy figures
A benchmark result applies to the model and evaluation setup that produced it. Before comparing figures, check the exact model and version, test questions and domain, whether browsing or retrieval was enabled, whether results count errors per claim or per response, how abstentions were scored, and who graded the answers.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsFor example, OpenAI’s GPT-5 System Card reports that GPT-5 main had a 26% smaller claim-level hallucination rate than GPT-4o, while GPT-5 thinking had a 65% smaller rate than OpenAI o3 in the card’s evaluations. These are OpenAI-reported comparisons tied to the card’s prompts, models, test conditions, and evaluation method; they do not predict the chance that any chatbot answer you receive will be wrong. The card also reports 75% agreement between its LLM grader’s factuality judgments and human judgments. That is a measure of grader agreement, not chatbot accuracy.
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.
