Why Does AI Make Things Up? Hallucinations Explained With Examples You Can Check
Why AI Sounds Certain When It Is Wrong
An AI Answer Can Sound Meticulously Researched Even When Its Most Important Detail Is Invented.
A chatbot supplies an author, a publication year and a perfectly plausible paper title. It explains the findings in tidy paragraphs. Then you search for the paper and discover that the citation does not exist, or that a real paper says something different.
This is the practical problem usually called an AI hallucination: a system generates information that is false or unsupported while presenting it as an answer. Large language models learn patterns that enable fluent, often useful responses. Those patterns do not guarantee that every generated claim corresponds to a verified fact.
Hallucinations are not confined to obscure trivia. They can appear in document summaries, calculations, quotations, technical instructions and explanations of current events. Understanding why they happen makes it easier to decide where AI can save time and where the answer needs an independent check.
What A Language Model Is Actually Producing
A language model generates sequences of tokens, the pieces into which its system divides text. During training, it learns statistical relationships that help it predict suitable continuations. Subsequent training can improve instruction-following, reasoning, tool use and other behaviours, but fluent completion and factual verification remain different tasks.
The model’s learned knowledge is not equivalent to a conventional database in which every sentence has an attached, retrievable source record. A generated answer may reflect knowledge acquired during training without being able to identify exactly where that information came from.
OpenAI’s 2025 research on hallucinations argues that ordinary training and evaluation practices can reward guessing instead of acknowledging uncertainty. If a test rewards correct answers but gives no advantage for declining an uncertain question, a system may gain by attempting an answer even when it sometimes invents one.
This is one explanatory account of a broad problem, rather than proof that every error has one cause. Wrong answers can also arise from poor retrieval, ambiguous questions, mistaken reasoning or outdated information. The useful question is where the particular answer lost contact with its evidence.
Three Different Failures That Look Similar
The first failure is a false factual statement: an invented date, nonexistent publication or incorrect description of an event. A reader may be able to resolve it by consulting an authoritative record. Specific detail does not make such a statement more reliable; it merely gives you something specific to check.
The second is an unfaithful account of supplied material. Suppose a document says a proposal is awaiting approval, while the summary says it has been approved. The summary is wrong even if the proposal later succeeds. It has misrepresented the evidence it was asked to describe.
The third is an unsupported inference presented as a fact. A report might record that sales rose after a campaign. An AI answer might say that the campaign caused the increase, despite the report providing no causal analysis. The original numbers can be correct while the conclusion exceeds them.
Distinguishing these failures improves the remedy. Checking a date will not repair a causal overclaim. Finding a real source will not repair a summary that reverses the source’s meaning. Different errors require different questions.
Why A Citation Is Only The Beginning Of Verification
A citation can fail in several ways. The source may be invented, the bibliographic details may be wrong, or the source may be real but irrelevant. Even an accurate link can be attached to a claim that the linked page does not support.
Verification therefore has two stages: establish that the source exists, then establish that it supports the precise proposition. Look for the relevant passage, table or result. Check whether the statement describes the source’s findings or expands them into a stronger claim.
For research, study design matters. A result in a laboratory experiment does not automatically establish a benefit in everyday use. A model tested on one dataset does not automatically perform equally well elsewhere. The citation must support the scope of the sentence, including its qualifications.
This is especially important when an answer contains several references. Quantity can create the appearance of corroboration, even when all the links repeat the same underlying announcement. Five links are not necessarily five independent checks.
A Source-Checking Exercise You Can Reproduce
The following is a deliberately constructed exercise, not a reported test of a named model. Its purpose is to make the correct answers inspectable without specialist knowledge. You can use it to observe how a tool handles missing information and tempting assumptions.
Give the tool this short source passage: “Harbour Museum opened its new gallery on 14 March. The project budget was £240,000. A final expenditure figure has not been published. The director described attendance as encouraging. No visitor total was provided.” Harbour Museum and these details are fictional.
Then ask four questions, requiring answers only from the passage: When did the gallery open? What was the project budget? How much did the project finally cost? How many people visited? The first two questions have explicit answers. The last two do not.
A faithful answer gives 14 March and £240,000, then says that final expenditure and visitor numbers are unavailable in the supplied passage. An answer that treats the budget as final expenditure has confused two categories. An invented attendance figure fills a genuine information gap with fabricated precision.
Make the exercise harder by asking: “Why did the gallery exceed its budget?” The passage never says that it did. A reliable response should identify the unsupported premise before attempting an explanation. This tests whether the tool follows the evidence when the wording of a question suggests a convenient story.
How To Record A Real Test Without Overclaiming
If you run the exercise, record the tool, displayed model label, date, exact prompt, source passage and whether browsing was enabled. Save the complete response, including any caveats. Those details allow someone else to understand what you actually observed.
Judge each answer separately. Mark whether it reproduces an explicit fact, appropriately declines to supply missing information, or introduces an unsupported claim. Do not award credit merely because the answer sounds cautious: a disclaimer followed by an invented figure still contains an invented figure.
Repeating the test can reveal variation, but this tiny exercise cannot establish a general hallucination rate. It is a diagnostic example of one failure mode. A credible comparison between models would need a larger, representative question set and a consistent evaluation method.
That distinction protects against a familiar reporting mistake. “The tool failed this question” is an observation. “The tool is wrong most of the time” is a much broader claim, requiring substantially more evidence.
Why Browsing And Retrieval Help
Retrieval-augmented generation, often shortened to RAG, gives a model relevant material to use while producing an answer. The original research by Patrick Lewis and colleagues combined retrieval with generation and found improvements in factuality on the tasks they studied compared with a particular baseline.
The practical advantage is straightforward: the system can consult information rather than rely entirely on what was absorbed during training. This is useful for current facts, private document collections and questions requiring specific source passages. It also makes a checkable evidence trail possible.
But retrieval introduces its own questions. Was the right document found? Is it current? Does it contain the answer? Did the model accurately represent it? Supplying evidence creates an opportunity for grounding; it does not guarantee that the opportunity was used correctly.
For a broader introduction to the technology, Taylor Tailored’s explanation of artificial intelligence and its limits provides context. The practical habit remains the same: inspect the evidence supporting the answer that matters to your decision.
Uploading A Long Document Does Not Guarantee A Faithful Summary
The research paper Lost in the Middle tested how models used information positioned in different parts of their input. In the evaluated tasks, performance could deteriorate when relevant material appeared in the middle of a long context. The findings concern the models and settings studied, rather than a permanent score for every later system.
The lasting lesson is methodological: capacity to accept a long input is not the same as demonstrated reliability across that input. A document can be available to a system while particular qualifications, exceptions or conflicting passages receive inadequate attention.
For important summaries, ask for the passage supporting each consequential conclusion. Check exceptions as carefully as main rules. If a report contains a provisional figure and a later correction, the summary needs to distinguish them rather than choose whichever number is easier to repeat.
Confidence And Explanation Are Not Proof
A model can write “I am certain” because those words fit the response it is generating. Unless a particular confidence measure has been tested and calibrated for the relevant task, a percentage or emphatic phrase should not be treated as a measured probability of correctness.
An elaborate explanation is also insufficient on its own. Anthropic’s research into chain-of-thought faithfulness examined whether generated reasoning reliably reflected factors influencing answers. It provides a reason to avoid treating a model’s explanation as a transparent audit of its internal process.
The useful alternative is an external check: inspect the source, rerun the calculation, execute the code in an appropriate test environment or compare the output with a known reference. The verification should address the task’s actual failure mode.
Build A Workflow That Makes Errors Visible
Before using an answer, separate claims that are easy to verify from judgements that require interpretation. Dates, quotations, totals and document status deserve direct checks. An argument or recommendation needs its assumptions made explicit so you can decide whether they fit your situation.
Ask the tool to distinguish sourced facts, calculations and inferences. Give it permission to say that information is missing. These instructions can help organise the response, but they are not a guarantee against fabrication. The user still needs a way to inspect the important claims.
For routine drafting, an error may be cheap to correct. For a consequential decision, the verification effort should rise with the cost of being wrong. The aim is not to distrust every sentence equally. It is to identify the sentences on which the decision actually depends.
AI becomes more useful when its output is easy to challenge. A polished answer can start the work, suggest a route or clarify a difficult passage. The moment it becomes evidence is the moment its claims have been checked against something beyond its own confidence.
Sources And Further Evidence
OpenAI, Why language models hallucinate (5 September 2025)—training and evaluation account
Liu et al., Lost in the Middle (2023)—position and long-context performance
