An AI model makes mistakes when it constructs a plausible answer from learned language patterns instead of drawing on reliable knowledge. As a result, it may confidently state something that is made up. To check its answer, compare the key claims with reliable primary sources.
These mistakes are called AI hallucinations: text can sound convincing without being true. Let’s look at why models invent details, which answers need extra scrutiny, and how to tell a useful suggestion from a confident mistake.
| Part of the answer | What to verify | Warning sign |
|---|---|---|
| Fact | Whether a primary source confirms it | No verifiable source |
| Number | Value, unit, and period | No unit or context |
| Date | The event and its date in the source | Date not tied to an event |
| Source | Whether it supports the claim in question | Source covers only a similar topic |
| Relevance to the prompt | Whether the text answers the question | General discussion instead of an answer |
- 6,000 questions The size of the AA-Omniscience benchmark from Artificial Analysis, mentioned in the Sostav article.
- November 2025 The period covered by the AA-Omniscience benchmark cited in the provided article.
- 3 models The number of models scoring above zero in the description of this benchmark.
What is an AI hallucination?
An AI hallucination is an unintentional model error caused by limitations in its data or algorithms, not a conscious attempt to deceive the user. This definition is given by Igor Bederov, director of investigations at T.Hunter, in a Kommersant article about AI hallucinations.
An error can hide in a single fact even when the entire answer sounds coherent and confident. For example, a language model may make a plausible claim without checking it against reality: it generates text based on patterns in its data rather than independently verifying whether the claim is true. So a convincing delivery does not, by itself, confirm that an answer is accurate.
A generation error is not the same as disinformation
It is important to distinguish hallucination from deliberate disinformation. The latter can result from system abuse—for example, a jailbreak, or an attempt to bypass restrictions built into a model. An ordinary error does not, by itself, mean that a user attacked the system.
The term “hallucination” describes the result of generation, not the AI’s intent. Even if a model confidently states something false, that does not prove it knows the answer is false: the cause lies in the limitations of its data or algorithms, and each specific claim should be checked separately.
Why does a language model confidently give wrong information?
A language model confidently gives wrong information because it selects a likely continuation of text rather than checking each claim against reality. It constructs an answer as a sequence of tokens—small units of text—and chooses the next one based on probabilities learned during training. A smooth phrasing therefore shows that the words fit together in context, but it does not prove that the stated fact is accurate.
Kod magazine explains that a model does not distinguish truth from falsehood or automatically check answers against a source. If a familiar pattern appeared in its training data—for example, a typical description of an event or a reference to a document—a similar template may show up in an answer without confirming the specific details. A confident tone is no more a form of verification: the model generates it along with the text.
Why specific questions are especially risky
The limitations of training data and algorithms become more apparent when a question calls for an exact detail the model cannot reliably confirm, such as a document title, date, or numerical value. In this situation, it may fill in the answer using a familiar pattern instead of acknowledging that it lacks sufficient grounds. That is why an answer’s coherence and the verification of its claims should be assessed separately: check specific details against a primary source rather than taking them on trust because they sound convincing.
What kinds of mistakes are there besides made-up facts?
Besides making up facts, AI can give an overly general answer, stray off topic, or substitute instructions for a result; hostile statements are another risk. These are different kinds of mistakes: an answer may consist of true claims and still fail to solve the user’s problem.
An article on 1ps.ru gives an example of substituting instructions for a result: instead of completing a task, the model explains how to do it yourself. It also notes off-topic answers—for example, general information in response to a specific request—and hostile statements, which can occur in some models, usually early versions. In these cases, checking individual names and dates will not reveal the main problem: the answer does not address the request or uses an unacceptable tone.
How to assess the answer as a whole
Check not only the facts but also whether the answer matches the original question. Compare the prompt and the result against three criteria:
- Result: Did the model complete the task, or merely explain how to do it?
- Topic: Does the answer address the specific question, or replace it with general information?
- Content: Does the answer contain any hostile statements?
If even one criterion is not met, ask the model to answer the original question directly, then check the new version against the same points.
How do you verify a fact, number, and source in an answer?
Check an AI answer claim by claim: list the names, dates, numbers, organization names, and cause-and-effect conclusions, then find support for each item in a primary source. If no source is cited or you cannot find one, treat the claim as unverified, not as fact.
Checking sources
A source cited in an AI answer does not, by itself, confirm what the answer says: open the page and check whether it addresses the specific fact being claimed. For example, if a model explains hallucinations as a result of limitations in data or algorithms, the source should support that cause, not merely mention AI hallucinations. A similar topic is not evidence for a specific conclusion.
Checking numbers and dates
For each number, establish the unit of measurement, period, and what is being counted: a percentage without a denominator and a date without context do not provide enough information to fully check a claim. The available description of the AA-Omniscience benchmark gives November 2025 and 6,000 questions; if a model cites these figures, compare them with the source and find out which test they refer to. Check cause-and-effect conclusions separately: confirming that two events happened does not mean one caused the other.
How can you prompt a model to reduce the risk of errors?
To reduce the risk of errors, ask the AI a narrowly defined question, specify the format you want in advance, and ask it to separate verified information from assumptions. For example, instead of “Tell me about AI hallucinations,” ask it to briefly explain why a language model might confidently state a false fact, or to compile a list of sources to check.
It is worth explicitly asking the model to say “I don’t know” if it has no reliable answer, and to flag claims it cannot confirm. This matters in particular because a language model generates text by predicting a sequence of tokens rather than checking each sentence against reality: a confident tone does not, by itself, prove accuracy.
How to check sources
If the model cites a source, ask for its exact title and an explanation of which claim it supports; then open the document and check the relevant passage yourself. If you cannot find the source or it does not support the claim, do not treat the citation as proof. For an important decision, such as one related to work or finances, use the AI answer only as a starting point: check the conclusion against the primary document rather than relying on the answer in place of verification.
When do checking and prompting fail to solve the problem?
Checking and prompting do not solve the problem if the model lacks verified information or continues to produce plausible wording instead of acknowledging uncertainty. Asking it to “answer confidently” changes the tone but does not add factual knowledge: a confident delivery does not, by itself, prove that a claim is true.
Even a source citation does not guarantee that it supports a specific number or quotation: open the material and compare the exact passage you need, not just the publication’s title. This matters when checking language model answers, too: they generate likely text continuations rather than checking every sentence against reality. If you insist on an answer at any cost, the model may produce a convincing continuation where it would be more appropriate to acknowledge that it lacks information.
What to do if there is no confirmation
If a primary source does not confirm a claim, treat it as unverified: clarify the wording, ask the model to separate facts from assumptions, or do not use the answer without independent verification. It cannot be guaranteed that hallucinations will be eliminated entirely: the vАЙТИ article describes this as a difficult challenge for artificial intelligence and suggests that it may not be possible to solve it completely. So check the substance of every important claim—not the model’s confidence or the mere presence of a citation.
Frequently asked questions
Is an AI hallucination an intentional lie?
Why does a model sound confident even when it is wrong?
How can I check a number given by AI?
Will asking for sources help?
Sources
- kommersant.ru — “AI Hallucinations: What They Are and Why Models Make Mistakes”
- 1ps.ru — “Neural Network Hallucinations: What Mistakes AI Makes and Why”
- Kod magazine — “AI Hallucinations: Why LLMs Lie and How to Fight It”
- vaiti.io — “How to Effectively Tackle Neural Network Hallucinations — vАЙТИ”
- sostav.ru — “Neural Network Hallucinations: How to Check Answers in Work Tasks”
