Listen to this post: Understanding Hallucinations in Generative AI (What They Are, Why They Happen, and How to Reduce Them)
A generative AI hallucination is when an AI model produces information that sounds plausible but is wrong, made up, or not supported by evidence.
That matters because hallucinations don’t look like “errors” in the way a broken calculator looks wrong. They often arrive wrapped in smooth language and a confident tone. They can spread quickly through email threads, presentations, social posts, and even reports, and they can cause real harm when people act on them.
This guide explains what hallucinations are, why they happen, how to spot them, and practical ways to reduce them at work and at home. The tone here is calm for a reason: even top models in 2026 still hallucinate on difficult tasks, but you can manage the risk with the right habits and safeguards.
What are hallucinations in generative AI, and what do they look like?
Generative AI predicts what text is likely to come next, based on patterns in training data. It doesn’t “know” facts in the human sense, and it doesn’t have built-in truth sensors.
A hallucination can be:
- Completely made up, like inventing a law, a product feature, or an event.
- Partly wrong, like mixing up two real studies or swapping dates.
- True but unsupported, where the answer might be correct, but there’s no reliable source behind it (and the model can’t show one).
The tricky part is that the writing can still be fluent, structured, and persuasive. The model is optimised to produce a good-looking response, not to guarantee it’s correct. That’s why hallucinations can read like a polished Wikipedia entry, even when the details don’t exist.
For a deeper sense of why the problem persists even now, the Duke University Libraries post https://blogs.library.duke.edu/blog/2026/01/05/its-2026-why-are-llms-still-hallucinating/ gives a clear, real-world view from a research and teaching context.
Common types of AI hallucinations (fake facts, bogus sources, wrong reasoning)
Most hallucinations fall into patterns you’ll recognise once you’ve seen a few.
Invented facts and stats: The model quotes a precise number (“market grew by 17.3%”) without any traceable source.
Made-up names, dates, or organisations: It creates a “Dr Jane Smith at Cambridge” who doesn’t exist, or places an event in the wrong year.
Fake citations, links, or references: It provides citations that look academic but can’t be found, or links that lead nowhere. Fabricated references remain a known pain point, and they can slip into academic writing workflows when people trust the format over the substance.
Wrong quotes: It attributes a quote to a real person, but the wording was never said or published.
Blended truths: It merges two real things into one, like mixing the methods of one paper with the findings of another.
Reasoning that sounds right but ends wrong: The step-by-step logic reads neatly, yet a subtle mistake early on poisons the conclusion.
Concerns about fake references aren’t hypothetical. GPTZero reported finding hallucinated citations in conference submissions under review, with peer reviewers missing them, in https://gptzero.me/news/iclr-2026/.
Hallucinations vs mistakes, bias, and outdated information
Not every wrong answer is a hallucination. It helps to separate four common failure modes:
| Issue | What it looks like | Typical cause | Quick response |
|---|---|---|---|
| Mistake | Typos, arithmetic slips, misread question | Human-style error in output | Re-check and correct |
| Bias | Skewed framing, unfair assumptions, stereotypes | Patterns in training data and prompts | Reframe, apply fairness checks |
| Outdated info | Old pricing, old policies, missing recent events | Knowledge cut-off or stale sources | Check current sources |
| Hallucination | Confident invention or unsupported “facts” | Next-word guessing without verification | Demand evidence and verify |
A simple test works well in practice: Can you verify it from a reliable source? If you can’t, treat it as untrusted, even if it sounds confident.
Why do AI models hallucinate, even when they sound confident?
If you’ve ever used autocomplete on your phone, you already understand the basic mechanism. It guesses the next word. Now scale that up to billions of learned patterns, and you get an engine that can produce extremely convincing text.
The confidence is mostly a writing style, not a truth signal. A model can sound sure because it has seen many examples of sure-sounding writing. That tone can appear even when the underlying claim is a guess.
Industry tracking still shows double-digit hallucination rates on hard evaluations in 2026, even for strong models. One place people often check for comparisons is https://research.aimultiple.com/ai-hallucination/, which summarises benchmarking and vendor claims, with the usual caveats about changing model versions and test design.
The core reason: next-word prediction, not built-in fact checking
Under the hood, most large language models work by predicting tokens (chunks of text). In plain terms:
- The model reads your prompt.
- It predicts the next token that best fits.
- It repeats that process until it reaches an answer.
It’s trying to produce a response that looks like a good answer. It’s not automatically checking each statement against a verified database.
This is also why models can be pushed into guessing. If a prompt signals “you must answer” or “don’t say you can’t”, the model often fills the gap with the most likely-sounding completion. Some newer models are trained to refuse more often, but the trade-off is real: if you reward always answering, you often increase confident wrong answers.
If you want a more technical overview of causes and mitigations, the survey at https://arxiv.org/html/2510.06265v1 is a useful map of the research terms and categories (it’s long, but searchable).
Real-world causes: messy data, vague prompts, missing context, and new events
Beyond the core mechanism, everyday conditions make hallucinations more likely:
Messy or conflicting training data: If the model has seen many versions of a “fact”, it may pick the wrong one, or blend them.
Vague prompts: “Tell me about that law” invites guessing. The model doesn’t know which country, year, or context you mean unless you say.
Missing context: If you paste half an email chain or omit key numbers, the model may try to “complete” the story.
New events: Anything that changed yesterday can be a problem if the model isn’t connected to up-to-date sources.
In 2026, even strong models still show noticeable hallucination rates on open-ended tasks and hard reasoning, which is why verification remains part of normal use, not a rare edge case.
How to spot AI hallucinations before they mislead you
A good mindset is simple: verify, slow down, demand evidence. You don’t need to treat every chat response like a court document, but you should match your checks to the stakes.
This also applies beyond text. Image models can invent “evidence” too, like fake product photos, impossible diagrams, or charts that look tidy but don’t match the numbers.
Red flags checklist: over-specific claims, shaky citations, and “too neat” answers
Watch for these signals, especially when the answer could affect money, health, safety, or reputation:
- Exact figures with no source (percentages, totals, dates, “latest” claims).
- Named studies you can’t find with a quick search.
- Citations that don’t match the claim, even if they look real.
- Links that 404 or lead to unrelated pages.
- Confident legal, medical, or financial advice delivered as certainty.
- Suspiciously tidy explanations, where every point lines up perfectly and there’s no mention of trade-offs or unknowns.
- Quotations with no publication details (no book, speech, date, or outlet).
A practical move: ask the model for the source, date, and method behind any claim. If it can’t provide them, treat the output as a draft idea, not a fact.
For examples of how hallucinations show up in normal usage, DataCamp’s guide is clear and approachable: https://www.datacamp.com/blog/ai-hallucination.
Quick verification habits: triangulate, quote-check, and sanity-test
You don’t need a perfect process. You need a repeatable one.
Triangulate: Check the claim against two trusted sources (official docs, reputable outlets, primary research, or your organisation’s internal policies).
Quote-check: If the AI provides a quote, search the exact phrase in quotation marks. If nothing credible appears, assume it’s made up or misquoted.
Verify names and dates: People’s job titles, release dates, and law names are common failure points.
Sanity-test: Ask, “Does this make sense?” If a “new regulation” claims to affect every UK business overnight, your common sense should kick in.
For technical topics, re-run the maths yourself. If the AI shows working, check each step, not just the final result. A neat chain of reasoning can still hide a wrong assumption.
For images and charts, zoom in and inspect details. AI-generated charts often include subtle problems like mismatched axis labels, inconsistent totals, or made-up sources at the bottom.
How to reduce hallucinations, practical fixes for everyday use and teams
You can reduce hallucinations a lot, but you can’t remove them fully. Think of generative AI as a fast first-draft partner that still needs supervision.
The good news is that small changes to prompts and workflow can cut the risk quickly.
Prompting techniques that cut errors (constraints, sources, and “say you’re unsure”)
A prompt doesn’t need fancy wording. It needs boundaries and expectations.
Try patterns like these (adapt them to your situation):
Set the scope: “Answer for the UK, and assume the reader is non-technical.”
Ask for assumptions: “List your assumptions before you answer.”
Require traceable evidence: “Only include facts you can support with a source, and include the source name and publication date.”
Ask for direct quotes when citing: “If you cite a source, include a short direct quote that supports the claim.”
Invite uncertainty: “If you’re not sure, say ‘I’m not sure’ and tell me what to check.”
Start short, then expand: “Give a 5-bullet summary first. Then expand only the parts I approve.” (This reduces the space for the model to invent filler.)
One more trick that works well: ask the model to produce a “verification list” at the end, a short set of claims that should be checked before use.
Stronger safeguards: retrieval tools (RAG), domain checks, and human review
For teams, the biggest gains come from putting guardrails around what the model is allowed to use and how outputs are approved.
Retrieval-augmented generation (RAG) helps because the model answers using a set of trusted documents, rather than its memory alone. In simple terms, it searches your approved content (policies, manuals, product docs, knowledge base articles) and then writes the answer from that material. This doesn’t make errors impossible, but it lowers made-up claims because the model has something concrete to ground on.
Other team safeguards that work in practice:
Approved sources list: Define which sources are acceptable for your domain (and which aren’t). This matters for regulated areas.
High-stakes checklists: Health, finance, legal, HR, and safety content should trigger extra review steps, even if the output looks fine.
Peer review: Treat AI-assisted work like junior-drafted work. It can be strong, but it needs oversight.
Logging for audit: Keep records of prompts and outputs for critical workflows. If something goes wrong, you need to know how it happened.
Testing with hard examples: Build a small set of “gotcha” questions that used to fail, then re-test after model updates.
Research on detection and mitigation keeps moving, including frameworks designed to reduce hallucinations during generation, but the direction in 2026 is clear: better training, cleaner data, and evaluations that reward honesty and refusal are active areas, not solved problems. If you want a research-heavy view of mitigation ideas, https://arxiv.org/abs/2507.22915 is a solid starting point.
Conclusion
Hallucinations in generative AI are confident outputs that are wrong, invented, or unsupported. They happen because models predict likely text, and that fluency can mask uncertainty. You can spot them by looking for red flags, demanding sources, and doing quick verification checks. You can reduce them with tighter prompts, retrieval tools like RAG, and human review for high-stakes work. Treat AI as a draft partner, verify important claims, and put strong safeguards around anything that could cause harm.
