What “making things up” means
Sometimes a chatbot states something that is simply false: a book that was never written, a quotation nobody said, a court case that does not exist, a function in a software library that has no such name. The error is often delivered in the same calm, polished tone as a correct answer. This behavior is commonly called hallucination, though “fabrication” or “confabulation” may describe it better, since the system is not perceiving anything. It is generating text that fits a pattern but does not match reality.
The root cause: plausible, not verified
A language model is trained to predict what text is likely to come next, as explained in how language models predict text. Its goal during training is to produce text that looks like the text it learned from, not to check each statement against a source of truth. When you ask a question, the model generates an answer that fits the form of a good answer. Most of the time, the likely answer is also the correct one, because correct information appears frequently in the training material. But the process has no built-in step that says “I do not actually know this.”
So when a question lands in an area where the model has weak or no information, it often still produces something that sounds right. A request for a reference naturally leads to text shaped like a reference: author names, a title, a journal and a year, all combined in a believable way.
Specific situations that raise the risk
- Rare or obscure topics. If a subject rarely appeared in the training data, the model has little to go on, so it fills gaps with plausible patterns.
- Requests for exact details. Dates, page numbers, statistics, legal citations and quotations need precision, and precision is where pattern-matching fails most visibly.
- False premises. If a question assumes something untrue, such as asking for details about an event that never occurred, the model may play along rather than object.
- Leading questions. Wording that suggests a desired answer can push the output toward it.
- Recent events. Without access to current information, a model can only draw on what it saw during training, which stops at a certain point.
- Long contexts. If the input is longer than the model’s limit or the key details are buried, parts may be missed, a limit related to context windows.
- Gaps and skew in the data. Errors and biases in the source material can be reproduced, which links to the quality of training data.
- Unsupported arithmetic and logic. The model may produce a calculation that reads well but contains a wrong step.
Why the tone misleads
Humans use confidence as a clue to reliability. With a chatbot, that clue is unreliable. The model’s fluency comes from having learned the form of good writing, so it sounds equally assured whether it is right or wrong. Some systems can be asked to express uncertainty, and doing so may help somewhat, but a stated confidence level is also just generated text and is not a dependable measure.
Efforts to reduce the problem
Developers work on several approaches, and none removes it entirely.
- Retrieval. Connecting a model to a trusted set of documents or to a search function so that answers are based on retrieved text rather than memory alone. This helps, but the model can still misread or misreport what it retrieves.
- Further training. Using human feedback to reward answers that admit uncertainty or decline to guess.
- Citations and links. Some tools show their sources so you can check them, although the cited page may not actually support the claim.
- Narrow, well-defined uses. Systems limited to a specific domain with controlled data usually fare better than general chatbots on open questions.
Progress is real, but it would be unwise to assume any current system is free of this problem.
How to check answers
- Treat specifics as claims to verify. Names, numbers, dates, quotations and references should be confirmed in a reliable source.
- Look up every citation. Search for the title and author. If you cannot find it, assume it may be invented.
- Ask for the basis, then check it. Request sources or reasoning, and verify them independently rather than trusting a reassurance.
- Cross-check with another source. Compare with a reputable reference, an official site or a qualified person.
- Rephrase and compare. Asking the same question in different words and seeing inconsistent answers is a sign of weak footing. Consistency alone does not prove correctness, though.
- Provide the source material. Pasting the relevant text and asking the model to answer only from it can reduce errors, although it does not eliminate them. How you phrase requests matters, as covered in AI prompts.
- Raise your standard for important topics. In health, law, finance or safety, use chatbot output only to prepare questions for a professional.
Where mistakes matter most
Errors are low-stakes when brainstorming names for a project and high-stakes when drafting a legal filing, a medical note, a news story or a school essay. There have been public cases in which people submitted AI-generated material containing invented citations and faced consequences. In customer-facing settings, an automated assistant that gives wrong information can mislead people, a concern that appears in discussions of support chatbots.
A balanced view
Fabrication is not a rare glitch that will be patched soon. It follows from how these systems work. That does not make them useless; they remain good at drafting, reorganizing and explaining when a person checks the facts. The sensible stance is neither blind trust nor blanket dismissal, but a habit of verification that matches what is at stake. This is the same caution that underlies responsible AI use.