Why does ChatGPT give wrong answers to company questions? Understanding hallucinations

ChatGPT gives wrong answers to company questions because it does not know your internal documents – and answers anyway. The model produces the linguistically most probable answer, not the verified truth: for questions about the procedures, terms or technical particularities of your company, it guesses, and phrases the guess convincingly. What helps is not cleverer questions, but an AI that looks things up in your own company documents before answering and names the source for every answer.

In brief

• Hallucinations are convincingly phrased but wrong AI answers – invented facts, procedures, figures or sources.
• ChatGPT was trained on publicly available texts; it does not know your internal rules, records and agreements.
• Instead of admitting a gap in its knowledge, the model delivers the most plausible continuation – for company questions, that is a guess.
• The more fluent an answer sounds, the more likely it is to be accepted unchecked; but quality of language is no proof of truth.
• RAG provides the remedy: the AI answers only from your own documents and backs up every answer with a source.

What are hallucinations in ChatGPT?

Hallucinations are answers that an AI makes up, even though they sound like established facts. They range from invented details and non-existent documents to sources that never existed. The treacherous part: linguistically, the invented answer is no different from a correct one.

To understand why this happens, it helps to look at how it works. A language model such as ChatGPT has learned from huge amounts of text which word follows which with what probability. It completes texts – very well, in fact. But it does not look anything up in a directory of facts, and it has no built-in mechanism that reliably says: “I don’t know that.”

A question is therefore always followed by an answer – the statistically most plausible one. Where knowledge exists, this answer is often correct. Where it is missing, the model fills the gap with what usually appears in similar texts. That is exactly what a hallucination is.

Why are company questions hit particularly hard?

ChatGPT was trained on publicly available texts – websites, books, articles. Your service records, internal rules, customer agreements and work instructions were never part of this training. The model simply cannot know anything that exists only in your company.

The dangerous part: to the model, company questions sound answerable. Asked “How do we handle complaints?”, it does not reply “no idea” but describes a typical complaints process of the kind found in thousands of texts – polite, structured, plausible. Except that it may have little to do with your actual process.

The same pattern appears with deadlines, responsibilities, technical settings or special agreements with customers: the model stitches together what is typical for the industry into an answer that is wrong for your company. The more specific the question, the more confidently it guesses – because the less likely it is that the answer appeared in public texts.

Why do wrong answers sound so convincing?

People unconsciously use quality of language as a signal of trust: anyone who answers fluently, in a structured way and without hesitation seems competent. With people, that is a useful rule of thumb – with language models, it is misleading. A model phrases the invented answer just as confidently as the correct one, because phrasing is exactly what it has learned.

In a company, this becomes a risk when answers are accepted under time pressure or when the person asking lacks the knowledge to recognise mistakes – new employees, for example. Wrong but confidently phrased information about operating a machine or a customer agreement is worse than none at all.

How can you recognise a hallucinated answer?

There is no absolutely certain sign – which is exactly what makes hallucinations tricky. Four checks work well in everyday use:

1. Ask for the source: Ask for the specific document the information comes from. For internal company matters, ChatGPT cannot name a real source – “sources” supplied afterwards are often invented themselves.
2. Distrust figures, deadlines and names: Specific values seem particularly credible and are particularly often guessed. Every figure needs to be cross-checked.
3. Do the cross-check: Ask the same question phrased slightly differently. If the answers differ in content, the model was guessing.
4. Apply the basic rule: ChatGPT cannot know anything that has been decided, agreed or documented internally. Answers to such questions are always assumptions.

How do you prevent hallucinations about company knowledge?

The most effective lever is to tie the AI to your own documents. The underlying method is called RAG (retrieval-augmented generation): before answering, the system picks out the relevant passages from the stored company documents and bases its answer exclusively on them – including the source. If it finds nothing, it says so instead of improvising.

Feature	ChatGPT without company data	AI knowledge base with RAG
Knowledge base	public training texts	your own company documents
When knowledge is missing	guesses plausibly	reports that nothing has been stored
Source	no real source possible	document including the source
How up to date	as of training	as current as your own documents
Checking	own follow-up research needed	one click to the source

The guide Using ChatGPT with your own data compares the ways of connecting ChatGPT and similar tools to your own records – from uploading individual files to a permanently connected knowledge base.

Do hallucinations disappear completely as a result?

No – and it is only honest to say so clearly. A RAG system can also summarise sources poorly, lose nuances or pick the wrong document out of several, for example when outdated and current versions sit side by side. Keeping the documents up to date remains the company’s job.

The practical difference is that answers can be verified: because every answer refers to a specific source document, checking takes one click instead of your own research. And because the system tells you when it finds nothing, the most dangerous kind of answer becomes much rarer – the confidently phrased invention.

What does this mean for using AI in your company?

For general tasks – writing texts, collecting ideas, explaining publicly available knowledge – ChatGPT remains a useful tool. For questions about your own company, a simple rule applies: answers without a verifiable source are assumptions and should be treated as such.

If you want to make company knowledge reliably accessible, you need an AI that bases its answers on your own documents, names its sources and processes the data on EU servers without using it to train third-party models. junova works on this principle – the How it works page shows how the roll-out works, including going through the answers to real questions with your own documents before go-live.