AI Hallucinations: Understanding and Avoiding Errors from ChatGPT, Claude and Copilot
An AI hallucination is a wrong, invented or unverifiable answer delivered with the same confidence as a correct one. You cannot remove them, but you can reduce them: give the model the right sources, frame your instructions tightly, and keep a human review on anything that leaves the company. This article explains why they happen and how to protect a small business against them, as a complement to our prompt engineering guide for SMEs.
What is an AI hallucination, in practice?
A hallucination is model output that looks plausible but is false or unfounded. The definition of AI hallucination covers three situations that people often mix up.
The first is pure invention: a legal reference, a quote, a market figure or a software feature that does not exist. The second is distortion: the model summarizes a document but changes an amount, a date or a condition. The third is false certainty: on a question where it should say "I don't know", it answers anyway.
What makes this tricky in a business setting is the tone. An invented answer shows no outward sign of doubt. A rushed employee copies it into a quote, meeting minutes or a client email without checking.
Why does a language model hallucinate?
An LLM does not look for the truth, it predicts the most probable continuation of a text. It learned language patterns from huge volumes of text, not a database of verified facts. When the requested information is rare, recent or missing from its training, it fills the gap with whatever "sounds right".
Several factors make this worse. Training data can contain errors or biases that the model reproduces. A vague instruction leaves the model free to extrapolate. A question built on a false premise ("why was this law repealed?" when it was not) often pushes the model to follow the premise rather than contradict it. And a very long document in the conversation raises the chance that the model loses a detail or mixes it up with another.
IBM, on its reference page on the topic, lists overfitting, biased or inaccurate training data and high model complexity among the causes. So treat hallucination as a property of how these tools work, not as a bug the next update will erase.
What are the risks for a small business or a nonprofit?
The main risk is not the error itself but its uncontrolled spread. These are the situations we see most often in companies:
- A quote or offer that reuses a price or condition invented by the assistant.
- A legal or regulatory note citing an article of law that does not exist or is misattributed.
- Meeting minutes where a decision or figure is attributed to the wrong person.
- An automatic reply to a client or member promising a service you do not offer.
- Published content (article, post, product sheet) containing a false claim that damages your credibility.
There is also a regulatory side. Regulation (EU) 2024/1689 (the AI Act) requires, in its Article 4, a sufficient level of AI literacy for the people who use AI in the organization, and knowing that a model can be wrong is part of it. We cover this in our article on Article 4 of the AI Act. On transparency, Article 50 of the same regulation applies generally from 2 August 2026 and notably concerns chatbots and AI-generated text published to inform the public, unless there is human review or editorial control, according to the European Commission's FAQ. For everyday use, see our article on the AI Act and ChatGPT.
How can you reduce hallucinations with your prompt?
The prompt is the first lever: the more precise the instruction, the less the model improvises. Four habits noticeably change answer quality.
Give the model the information instead of asking it to recall it. Paste the reference text into the conversation and ask it to answer only from that text. A typical instruction: "Answer only from the document below. If the information is not in it, write: information not in the document."
Explicitly allow "I don't know". By default, a model tries to be helpful and prefers to answer. A sentence like "If you are not sure, say so and state what is missing" cuts down invented answers.
Ask for sources and exact passages. Requiring a verbatim quote from the document, with its location, lets you check in ten seconds. Be careful: a model without internet access can fabricate a source that looks real, so a person must always verify it.
Break the reasoning into steps. For an analysis or a calculation, ask for the steps first and the conclusion after, as explained in our article on chain of thought prompting. Also fix the output format and give an example of a good answer with few-shot prompting. The basics of a good instruction are in the anatomy of a good prompt, and the difference between a standing instruction and a one-off request in system prompt vs user prompt.
Are RAG and technical guardrails enough?
RAG strongly reduces invention on your internal topics, but does not eliminate it. The principle: before answering, the system retrieves the relevant passages from your documents and inserts them into the instruction. The model then relies on your procedures, contracts or product sheets rather than its training memory. Our RAG vs prompting comparison helps you decide when to add it.
RAG has its own limits. If the source document is outdated or contradictory, so is the answer. If the search returns the wrong passage, the model will answer confidently from the wrong passage. Hence the value of complementary guardrails:
- An enforced output format (table, required fields) to spot gaps quickly.
- Source passages displayed next to the answer so the user can check.
- A confidence threshold or a refusal rule when no relevant passage is found.
- A content filter for topics the assistant must not handle (legal, medical, pricing).
These design choices matter as much as the model you pick. They belong in an AI audit for SMEs when you deploy an internal assistant or a customer chatbot.
What organization should you put in place to limit the risk?
The most effective rule fits in one sentence: the more exposed the output, the stricter the human review. An internal draft can be read quickly. Content sent to a client, published online or used in a management decision must be checked point by point.
In practice, three measures are enough for most small businesses and nonprofits. First, a one-page AI usage policy listing authorized uses, the cases where review is mandatory and the data that must never be entered. Second, a simple verification rule for any figure, quote or legal reference: cross-check it against a primary source before using it. Third, shared feedback: when an error is found, note it along with the prompt that caused it, and adjust the team's instructions.
Confidentiality comes up in parallel, because people often fix a hallucination by pasting more context into the tool. See our data confidentiality best practices for AI. And if your teams already use unapproved tools on their own, the risk of unchecked errors grows, as we explain in the article on shadow AI.
How do you train your teams to spot hallucinations?
A team that knows a model can be wrong checks naturally; a team that does not know it trusts. Training therefore plays a direct role: it teaches people to recognize warning signs (an overly precise reference, a round number with no source, an answer that flatters the question), to write instructions that limit improvisation, and to verify efficiently.
An effective format is to have participants work on their own cases: they produce an answer with a tool, then the group hunts for errors together. This is the approach we recommend, and the one you will find in the prompt engineering training and the operational AI program. If technical terms put you off, the glossary explains what a prompt and a token are; it all comes together in the prompt engineering guide.
How GrowthPerf can help
GrowthPerf is a Qualiopi-certified training provider specializing in AI, no-code and automation for SMEs and nonprofits. On hallucinations, we work at three levels: auditing your current uses and the associated risks, writing your instructions and review rules, and hands-on training of your teams on their own business cases.
If you want to know where your AI uses already expose your organization, request an AI audit or explore our AI for business program.
Frequently asked questions
Can AI hallucinations be removed completely?
No, only reduced. The very way a language model works (predicting probable text) makes error possible. Good prompting, RAG and human review lower the risk but do not cancel it. The right goal is to control what goes out, not to believe in an infallible tool.
Does ChatGPT hallucinate more than Claude or Copilot?
All language models hallucinate, to degrees that vary by task and version. The gaps change with every update, and published rankings age quickly. For business use, test the tool on your own documents rather than relying on a general ranking.
How do you spot a hallucination in an answer?
Be wary of very precise details you cannot find elsewhere. An article number, a quote in quotation marks, an exact date or a figure with no source are the most frequent cases. Look the item up in a primary source. If it exists nowhere, treat it as invented.
Does RAG solve the problem?
It reduces it on your internal topics but does not eliminate it. If documents are outdated or the search returns the wrong passage, the answer will be wrong. Always display the source passages so the user can check.
Is an employee liable if they pass on a false AI answer?
Content that is distributed engages the organization that publishes or sends it, whether a person or an AI wrote it. Individual liability depends on the context and the applicable law. If you want to formalize this in an internal charter, have a lawyer validate it. The safest approach is to set a clear review rule.
Should you ban generative AI to avoid errors?
No, a ban mostly pushes use underground. It is better to set a framework: authorized uses, forbidden data, mandatory review depending on exposure. It is more realistic and more protective.
What is the first thing to do tomorrow?
Paste the source into the conversation and ask the model to answer only from it. Add the instruction "if the information is not in the text, say so". This simple change removes a large share of inventions on summarizing, rewording and extraction tasks.