A hallucinating model might invent a statistic, cite a paper that doesn’t exist, attribute a policy to the wrong organisation or describe a service you stopped offering in 2019. The answer may sound entirely assured, which makes the error harder to spot.
A language model isn’t mistaken in the way a person is mistaken; it’s producing plausible continuations of text, and a fluent invention is often more plausible than an awkward truth.
Why it matters for content
The model, its training, the task and the material it’s given all affect the risk of hallucination. Content teams can improve the part they control: clear, current sources, unambiguous wording and fewer contradictory versions. These support grounded answers, but they don’t guarantee them.
Errors carry particular costs in eligibility, medical and legal information, prices, deadlines and safeguarding. In those areas, a confident wrong answer is worse than no answer at all.
How hallucination changes the picture
It changes what review is for. Traditional editing catches clumsy writing, inconsistency and errors of judgement. Reviewing AI-assisted content also means verifying things that read perfectly: that the quoted figure exists, that the cited source says what it’s claimed to say, that the named person holds the role attributed to them. Fluency isn’t evidence that an answer is true.
It also changes what counts as published content. If an assistant answers questions using your pages, PDFs, help articles and knowledge base entries, those sources can shape responses you haven’t approved individually. Teams may inspect logged or sampled answers, but can’t always review every interaction. Some of the work shifts upstream: fix the sources, retire contradictions and make the canonical version clear.
Retrieval helps considerably – grounding answers in specific documents rather than general training – but it doesn’t abolish the problem. Grounded systems still summarise, compress and occasionally stitch two unrelated passages into one plausible hybrid. High-stakes content needs layered controls: approved sources, checks against evidence, testing, appropriate specialist review and a route to abstain or escalate. Human review matters, but it can miss errors too.
Questions people ask
- Will better models fix this?
Results vary by model and task. Better training, retrieval and evaluation can reduce errors, but a newer or more accurate model isn’t automatically less likely to guess. Test the system on the work you need it to do, including whether it acknowledges missing evidence.
- Is it our fault or the model’s?
Often both, and the half you control is your content. Contradictory, outdated or missing sources can lead to retrieving or generating a misleading answer.
- How do we spot hallucinations?
Check the checkable: names, numbers, dates, quotations and citations. Ask for sources, then open them.
- Does this mean we shouldn’t use AI for content?
You can use it, with controls proportionate to the consequences. Check factual claims against evidence and put specialist review or escalation where an error could cause harm.
Learn more
- Why language models hallucinate – a clear account of why evaluation can reward guessing over admitting uncertainty.
- A survey of hallucination in large language models – causes, detection, mitigation and open questions, including the limits of grounding answers in retrieved material.
- AI Hallucination Cases Database – Damien Charlotin’s running record of court decisions worldwide in which parties relied on hallucinated AI content.