AI Hallucinations: Why Chatbots Make Things Up — and How to Catch Them
Photo: Hippopx (public domain). The answer sounded completely sure. That is exactly the problem.
Ask a chatbot for the biography of a person who does not exist and it may give you one: birthplace, career, achievements, all invented, all delivered in the tone of an encyclopaedia. Ask for academic references on a topic and it may produce real-sounding papers by real-sounding authors that were never written. This behaviour is called hallucination — the model generating text that is fluent, plausible and false.
Why it happens: prediction is not memory
A language model does not look answers up. As our guide to how large language models work explains, it predicts the most likely continuation of the text, token by token. Training teaches it what true statements look like — and the look is what it reproduces. A citation looks like Author, A. B. (2021). Title. Journal, 14(2), 113–129, so when asked for citations, the model can manufacture that shape without the paper existing.
Three pressures make hallucination structural rather than accidental:
- The model must answer in form. It is trained to be helpful and complete; “I don’t know” was historically under-rewarded compared with a confident response, so the system fills gaps with the most plausible material available.
- Its knowledge is compressed and frozen. Training squeezes a vast corpus into patterns. Detail blurs — especially rare facts, recent events, and anything it saw only once — and what blurs gets reconstructed, not retrieved.
- It has no internal fact-checker. Nothing in the generation loop compares a sentence against the world. Confidence in the output reflects how typical the wording is, not how true it is.
Newer models hallucinate measurably less, and models connected to live search can check as they go. Neither removes the failure mode; both change how often you meet it.
The common forms
- Invented sources. Fake papers, books, URLs and legal cases, formatted perfectly. The most documented and the most dangerous for students and professionals.
- Wrong specifics inside right answers. The summary is accurate; the date, figure or name inside it is not. These pass casual review because everything around them checks out.
- Invented features and policies. A software setting, a refund rule or a product specification that sounds entirely reasonable and does not exist.
- False balance and false certainty. Presenting a settled question as open, or a guess as a consensus — a hallucination of tone rather than of fact.
- Agreement drift. Models tend to accept a false premise in your question and build on it. Tell a chatbot a wrong “fact” confidently and it will often elaborate rather than correct you.
Where it matters most
Hallucinations are a nuisance in a birthday-toast draft and a liability anywhere else consequential: medical information, legal citations, financial figures, code that runs against real systems, and news or academic work. The rule of thumb: the more a wrong answer would cost you, the more witnesses you need beyond the chatbot. Courts have sanctioned lawyers for filing briefs with AI-invented cases; the judges were not persuaded by the confidence of the prose.
A working routine for catching them
- Ask for sources — then open them. Never accept a citation you have not clicked. If the link fails or the paper’s title finds no match in a search, treat the whole answer as unverified.
- Search one specific claim verbatim. Pick the answer’s most checkable fact — a number, a date, a quote — and search it exactly. Hallucinations dissolve under one precise query.
- Ask the same question differently. Rephrase and re-ask in a new conversation. Real knowledge is stable; invented detail tends to shift between tellings.
- Use a search-connected mode for factual questions. Models that browse can ground answers in pages you can inspect — and you should inspect them, because models also misread real sources.
- Invite uncertainty. “Tell me what you’re unsure about” and “what would change this answer?” reliably surface the soft spots the default tone hides.
- Be extra careful with the obscure. The rarer the topic — a small company’s history, a niche regulation, a minor historical figure — the thinner the training data and the richer the invention.
Frequently asked questions
Will hallucinations be fixed completely?
Almost certainly not, because they are the flip side of what makes the models useful: generating novel, fluent text beyond what was memorised. Expect steady reduction, better self-flagging (“I’m not certain about this”), and better tooling for verification — not elimination.
Can I tell from the writing when an answer is hallucinated?
No reliable tell exists. Hallucinated text is usually as smooth as accurate text — that is the definition of the problem. Specific, checkable claims appearing without sources are a prompt to verify, whether the answer is right or wrong.
Why does the chatbot apologise and then repeat the error?
The apology is also generated text, not an inspection of what went wrong. Correcting the model in the same conversation helps sometimes, but a persistent error is a signal to verify externally, not to argue longer.
Are some chatbots immune because they search the web?
No. Search grounding reduces invention about current facts, but models can still cite pages that do not support the claim, misread figures, or blend sources. The sources list becomes your checklist — use it.
The bottom line
A chatbot’s confidence is a style, not a measurement. Hallucination is what that style looks like when the facts run out — plausible text filling a gap you cannot see. Work with these systems accordingly: enjoy the fluency, verify the specifics, and make any answer that matters earn a second source.
Sources
- FreshSynapse — “What Are Large Language Models? A Plain-English Guide”: https://freshsynapse.com/articles/what-are-large-language-models/
- OpenAI — ChatGPT help and documentation: https://help.openai.com/
- Anthropic — Claude documentation: https://docs.anthropic.com/
