Season 2026 · Episode 3 · Aug 30, 2026
How to handle or identify wrong information from an AI LLM Model like ChatGPT or Claude?
Zareef Ahmed shares practical steps to spot and correct wrong information from AI models like ChatGPT and Claude, stressing that you are the quality control.
Show notes
Almost everyone who uses ChatGPT, Claude, or any other AI model has run into this problem at some point: the AI gives you information that is just plain wrong. In this episode, Zareef breaks down a simple, practical approach to spotting and handling wrong information from LLMs, and why you need to stay in the driver's seat when it comes to quality control.
- Always take the AI disclaimer seriously, it can and will make mistakes
- Separate facts from reasoning, facts like dates or measurements are verifiable, reasoning is not the same thing
- Prompt the AI upfront to verify its own suggestions and check for conflicts with existing systems
- Always ask for sources, but never trust a source blindly, check if it's actually reliable
- Rely on primary sources whenever possible
- Ask the model to state its confidence level, but remember research shows AI tends to be more confident when it's wrong
- Challenge every answer, ask if it's sure, ask it to verify its own claims
- Do independent verification and check internal consistency
- Remember you are the quality control, not the AI
- Understand that AI is a next token generation system, not a fact machine
- Rely on AI for intelligence and capability, not as a source of guaranteed facts
Transcript
Hello everyone, welcome back to another episode of Question Minutes with Zareef Ahmed. Today we are going to talk about a very interesting topic, and almost everybody has gone through this problem. So the question is how to handle or identify wrong information from an AI LLM model like ChatGPT or Claude or any other LLM model.
So my simple answer is that first of all, there is always a disclaimer attached to any AI LLM model that it can make mistakes, and you should always consider that disclaimer seriously. You should always consider that as a final statement from any AI model.
Number one thing that is required to identify if your LLM has given you wrong information, or if you want to correct the wrong information given by it, is that you always separate the facts from the reasoning. You should know what is verifiable or what is not verifiable. Like if it was a birthdate of a specific person, if this specific bottle contains this milligram of medicine, or anything related to numbers or something, these are the things which can be classified into the category of the facts. These can be verified.
But before going into that, because that comes after an output has been generated from LLM. But before even, when you are asking for output from your LLM model, it's always good to give a good prompt asking that AI should verify whatever it is suggesting, like if you are asking for suggestions to improve a specific software. So you should also ask that please also verify your suggestions if they are already into the system or not. And please also check if those systems will not impact any other part of the application. You should always give it to verify things around that.
And most importantly, you should ask for the sources, that whatever you are suggesting or whatever information you are giving, especially the facts, you should give the source. But even when you are asking for the source, you shouldn't trust the source blindly. You should identify if the source is really reliable or not.
Remember, I usually say that with AI, you are the quality control. AI is doing something for you, and it is your responsibility to do quality control on that.
Apart from that, for the information, you should always try to rely on the primary sources also, or only, I can say. And you can also ask the model to describe the confidence that it has in the information. But remember, it has been established through multiple research that when an LLM model is wrong, it is more confident. So you should be aware of that. And you should always challenge the answer. At least whatever answer has been given, you can always say, are you sure? Can you verify the claim that you are making? And please always do independent verifications, and you should always check the internal consistency.
And on top of that, intelligence is not about checking facts or getting the facts. One of the major misconceptions about AI is that it knows everything. No, it doesn't know everything, especially the facts, that it can mismatch, because it's a next token generation system. It's not a kind of fact machine. It's not a fact machine. Remember, again, I'm repeating, it is not a fact machine.
You should rely on the intelligence of the AI. But for the factual information, you should always challenge it. You should always go to primary sources. And please don't ever try to test the AI, especially on the facts. It can be wrong. It is always given as a disclaimer, so it can be wrong.
Don't just try to make any, even three, four years back, even I was making lots of jokes around the kind of information it was giving. Even I have made some videos around that. But over the time, I have realized, and I have conceded, that AI is not a fact machine. AI, as the name says, artificial intelligence, it is more about intelligence. It is more about doing things with your data, with your things, which a normal human cannot do easily or with that kind of precision or speed.
Thank you. In next episode, we will try to answer any other question around that. Till then, it's Zareef Ahmed. Thank you for listening.