Why Does AI Make Things Up? A Plain Guide for Law Firms
By Stacey Tallitsch | October 12, 2026
You asked an AI tool a simple question. It gave you an answer that sounded perfect. Confident, clean, well written. Then you checked it, and the answer was wrong. Not a little wrong. Made up. If that has happened to you, and you are wondering why does AI make things up, you are not imagining the problem.
There is a name for it. People in the industry call it a "hallucination." That is the word for when an AI tool states something false as if it were solid fact.
This is one of the most confusing things about AI for a business owner. The tool sounds so sure of itself. So how can it be wrong, and can you trust it with real work? Let me walk through it in plain language, and then show you the simple line that keeps you safe.
Why does AI make things up?
First, a quick definition. Most of the popular AI tools, like ChatGPT, run on something called a large language model. A large language model is a computer program trained to predict the next word in a sentence. That is the whole trick. The program read an enormous amount of text and learned the patterns of how words tend to fit together.
It is not looking anything up. This is the part that surprises people. When you ask it a question, it is not searching a filing cabinet of facts. It is predicting what a good answer would sound like, one word at a time.
Think of it like a very well read employee who never says "I do not know." Ask them anything and they give you a smooth, confident reply. Most of the time they are right, because they have read so much. But when they do not actually know, they do not stop. They guess. And the guess comes out sounding exactly as confident as the truth.
That guess is a hallucination. The AI is not lying to you. Lying means knowing the truth and hiding it. These tools do not know the difference between true and false in the first place. They only know what sounds right. OpenAI, the company behind ChatGPT, explains it this way: the models get rewarded for guessing rather than for admitting when they are not sure.
If you want the deeper version of why this happens, I covered how these tools get built in a plain guide to what "training data" really means. The short version is that the tool learns patterns, not facts. A tool built on patterns will sometimes produce a pattern that looks true and is not.
A real example from the legal world
Here is where this gets expensive, and it is a story every law firm should know.
In 2023, two New York lawyers filed a court brief in a personal injury case against an airline. To save time, one of them used ChatGPT to find supporting cases. The tool handed him several. They had names, quotes, and citation numbers. They looked exactly like real case law.
They were not real. The AI had invented them. Fake case names, fake quotes, fake citations, all made up, and all stated with complete confidence.
The other side went looking for the cases and could not find them, because they did not exist. The judge, P. Kevin Castel, called the filings "bogus." The two lawyers were fined $5,000 and ended up in news stories around the world.
Here is the part that matters most for you. The lawyer was not careless in the way you would expect. He even asked the tool whether the cases were real, and it told him yes. He trusted the confidence. That was the mistake. The tool will defend a made-up answer just as firmly as a true one, because it cannot tell the two apart.
And this was not a one-time event about a single careless lawyer. Courts have seen a steady run of these filings ever since. The pattern is always the same. A smart professional, short on time, trusted a confident answer and did not check it.
Why the better tools still do it
A common belief is that this is a bug that will get patched soon, or that paying for the premium version makes it go away. Neither one is true.
Hallucinations are not a glitch. They come from the basic way these tools work. A tool that predicts the next word will sometimes predict a wrong one. The newer and more expensive models hallucinate less often than the old ones did. They do not hallucinate never. There is a real difference between less and none, and your safety plan has to assume the gap exists.
A second belief is that you can report a wrong answer and get it corrected, like fixing a typo in a shared document. You cannot, at least not in the moment. The tool is not reading from a master file you can edit. It builds a fresh answer every single time you ask, so the same question can even get a slightly different answer twice.
So the question is not "which AI tool never makes things up." There is no such tool today. The real question is how you use any of them without getting burned.
When you can trust it, and when you cannot
Here is the practical line I draw. It holds up well for a law office, an accounting practice, or any firm where being wrong carries a real cost.
AI tools are strong when you hand them the facts and ask them to work with those facts. Paste in a client email and ask for a polite reply. Paste in a contract clause and ask what it means in plain English. Give it your rough notes and ask for a clean first draft. In jobs like these, the facts come from you, so there is almost no room for the tool to invent.
AI tools are risky when you ask them to supply facts you do not already have. Court cases. Statutes. Filing deadlines. Dollar figures. Names and dates. Anything where a confident wrong answer would cause real harm. That is exactly where hallucinations live.
So the rule is short enough to tape to your monitor. Use AI to work with information, not as your source of truth. If an answer contains a fact you would stake your reputation on, confirm it against a real source before it leaves your office. For a law firm, that means a real legal database, not the chatbot. For an accountant, it means the actual tax code or the client's actual numbers.
This is also why feeding sensitive client matter into these tools deserves its own caution. I wrote about that in whether it is safe to put your business information into AI tools, and it pairs well with this piece.
None of this means AI is a poor fit for your practice. The opposite is true. Used the right way, it gives you back real hours every week. I walked through some of those uses in how accountants use AI to answer client questions faster, and the same thinking carries over to a law office or any professional firm.
So, why does AI make things up? Because it was built to sound right, not to be right. That is not a reason to keep it out of your firm. It is a reason to keep your hand on the wheel. Let the tool carry the load on the drafting and the first-pass thinking. You stay the person who checks the facts. Do that, and you get the speed without the $5,000 surprise.
-- Stacey | The Standalone
About the Author
Stacey Tallitsch runs The Standalone, an AI Implementation Diagnostic practice for small business owners. He has 30 years of experience in technology and has written 21 books on systems thinking and decision-making. More than 30,000 students have learned from his online courses.
- Stacey Tallitsch, The Standalone