Confident, smooth and wrong
Anyone who uses a chatbot for long enough meets the moment when it states something that is simply not true. It might describe a book that does not exist, attribute a quotation to the wrong person, cite a court case no one can find, or explain a feature that a product does not have. The tone stays calm and assured throughout.
These errors are commonly called hallucinations, though the word is a loose metaphor. The software is not seeing things. It is doing what it always does, producing likely-sounding text, in a situation where likely-sounding and true have come apart.
The core reason: prediction, not lookup
A chatbot built on a language model generates a reply one small piece at a time, each time estimating what text would plausibly come next given the conversation so far. This is how generative AI works at its core. The model learned those patterns through machine learning on huge amounts of text.
Two consequences follow.
It is optimised to sound right. Training rewards text that resembles real human writing, and later tuning rewards answers that people rate as helpful. Neither process guarantees that each statement was checked against reality. A made-up case citation looks like a real one because the format is a pattern the model has seen many times.
It does not know what it does not know. A person who does not know an answer can say so, and might feel uncertain. A model has no built-in feeling of uncertainty that reliably governs what it writes. When the right answer is absent or weakly represented, the machinery that produces text still runs, and it fills the space with something shaped like an answer.
Developers work to reduce this, for example by training models to decline when unsure or by letting them search for information. Such measures help, but none removes the problem completely.
When errors are more likely
Some situations raise the odds of a fabricated or wrong answer.
- Obscure topics. Little coverage in the training data means little to anchor the response.
- Very specific details. Exact dates, page numbers, statistics, prices, phone numbers and legal citations are easy to get slightly wrong.
- Requests for sources. Asking for references can produce plausible-looking titles, authors and links that do not exist, or that exist but do not say what is claimed.
- Recent events. The model’s knowledge stops at a cutoff, and without a search feature it may describe old information as current.
- Leading questions. If your question assumes something false, the model may go along with it instead of correcting you.
- Long, multi-step reasoning. Small mistakes can accumulate across steps, and arithmetic is a classic weak point.
- Summaries of material it cannot see. Asking it to summarise a document or web page it was not actually given can produce an invented summary based on the title alone.
- Questions about itself. Descriptions of its own abilities, version or inner workings can be unreliable.
Why it matters
Errors are inconvenient when you are drafting a casual email. They matter more when the answer feeds into health, money, legal, safety or academic decisions. Fabricated material can also spread: a false claim pasted into a post or a report may be repeated by others, adding to online misinformation. In education, students and teachers alike need to be careful with machine-written references, a theme that runs through debates about technology in education.
A routine for cross-checking
You do not have to distrust everything. A proportionate routine works better than blanket suspicion.
- Decide how much accuracy you need. Brainstorming names for a club needs little checking; a medication question needs authoritative sources and, ideally, a professional.
- Verify the load-bearing facts. Pick the names, numbers, dates and claims the rest depends on, and confirm each in a reliable source.
- Check that sources exist. Search for the exact title or case. If you cannot find it independently, treat it as unverified, even if it sounds familiar.
- Open the source and read it. A real reference can still be misdescribed, so confirm it says what the chatbot claims.
- Compare more than one source. Agreement among independent, reputable references is more reassuring than a single confident answer.
- Ask again differently. Rephrase the question or ask in a new conversation. Inconsistent answers are a warning sign, although consistent answers are not proof.
- Ask the model to show uncertainty. You can ask it to say which parts it is unsure of or to separate firm facts from guesses. This sometimes helps, but its self-assessment is itself fallible.
- Use better tools for the job. For calculations use a calculator or spreadsheet. For current facts use a reliable search or the official source.
Using the model’s strengths
Chatbots are generally more dependable when the facts are supplied in your prompt. Asking one to rewrite, summarise or restructure a text you paste in tends to be safer than asking it to recall facts from memory, though it can still misread or omit things, so skim the result against the original. Careful prompt writing also helps, but it does not turn a prediction system into a fact checker.
A mindset that works
Treat a chatbot like a well-read, fast-talking colleague who sometimes bluffs. It is very good for first drafts, explanations, ideas and getting oriented in a subject. It is a poor final authority. Fluency tells you about the writing, not about the facts, and the habit of checking what matters is what keeps these tools useful.