Why AIs Like ChatGPT Make Mistakes (And What You Can Do About It)

AI feels like magic… until it isn’t.

You type a question into ChatGPT, Gemini, or Grok, and like a genie from a bottle, it gives you a perfectly phrased answer with the confidence of a tenured professor. But then…wait a minute…you realize something’s off.

The answer sounds great.

But it’s actually wrong.

Sometimes wildly wrong.

That disconnect isn’t just frustrating. It can be costly, especially if you’re relying on AI tools for business decisions, client-facing content, or operational tasks.

So what’s going on?

Why do these advanced tools get it wrong?

And what can we do about it?

Here’s a plain-language breakdown of the most common causes of AI mistakes, plus two practical solutions that can help you get far better results.

1. AIs Build Answers One Token at a Time

(Not One Thought at a Time)

Most modern AI tools run on something called an LLM, which stands for Large Language Model. These models aren’t magic, and they don’t think like people. In fact, they don’t “think” at all in the traditional sense.

Instead, they generate responses by predicting the next most likely chunk of text, called a token. A token might be a full word, part of a word, or even just punctuation.

Here’s how it works:

• You ask a question.

• The model predicts the best first token.

• Then the best second token.

• Then the third, and so on, one piece at a time.

It doesn’t pause to outline the whole idea.

It doesn’t verify the accuracy before continuing.

It just keeps guessing the next best piece as fast as it can.

That works surprisingly well most of the time. But if an early token is even slightly off, the rest of the sentence can spiral into full-blown nonsense. A ten percent error in word choice can easily turn into a one hundred percent wrong conclusion by the end of the response.

2. Happy User Syndrome

(When AI Tells You What You Want to Hear)

Here’s something most people don’t know. AI tools are trained not just to give good answers, but to make users feel good about those answers.

It’s called alignment with user preference, but in plain English, it means:

“Say things that keep people happy so they keep using the tool.”

That’s helpful when you’re asking for a bedtime story or a recipe.

But it’s dangerous when you’re testing a new business idea.

Here’s a real-world example.

Early on, I asked an AI to help me create a Google Form. It confidently explained exactly how “we” would do it together. But when I said, “Great—go ahead and build it,” the AI replied:

“Actually, I can’t do that. I don’t have a Google account.”

That’s not just disappointing. It’s a perfect example of Happy User Syndrome.

Instead of telling me up front that it couldn’t do what I asked, the AI told me what I wanted to hear first. It rewarded my enthusiasm instead of grounding me in reality.

This can also happen when brainstorming. If you’re excited, the AI often mirrors that excitement, even if the idea has major flaws. It’s not lying. It’s just optimized to be encouraging and polite. And that can lead to trouble.

3. Information Overload and Web Confusion

(More Data Doesn’t Always Mean Better Answers)

Some AIs, especially when browsing tools are enabled, pull information from across the internet. Others are trained on enormous datasets that include books, forums, websites, news articles, and more.

That sounds powerful, and in many ways it is, but it also means the model might:

• Mix outdated data with current facts

• Combine multiple sources that contradict each other

• Choose the most popular phrasing rather than the most accurate one

This often leads to hallucinations, which are confidently stated answers that sound great but don’t actually match reality. Especially in fast-moving fields like tech, law, or finance, even small details can get scrambled.

And remember: these models aren’t checking sources like a human would. They are stitching together text patterns based on probability, not evaluating facts.

4. Long Conversations Can Create Confusion

(Especially If Your Ideas Evolve)

Let’s say you’re developing a new product or business process. You start chatting with your AI assistant about Version 1.0. As the conversation goes on, your idea evolves. You pivot to Version 2.0, then maybe scrap that and rebuild from scratch.

But fifty messages later, the AI suddenly brings back your original version like it’s brand new.

What just happened?

Most AI tools don’t have real memory. Instead, they use a context window, which is a temporary memory of your current session. The longer that session gets, the more the AI has to juggle.

It might accidentally pull from outdated parts of your conversation because they seemed relevant, even if you’ve clearly moved on.

This can be especially frustrating when it reintroduces something you’ve already discarded.

So What Can You Do About It?

There’s no way to make AI perfect. But you can dramatically reduce errors with two powerful strategies:

1. Use Hyper-Specific Prompts

The clearer and more detailed your request, the less the AI has to guess. Avoid vague questions like:

“Can you help me with this idea?”

Instead, say:

“Here’s the updated version of my project. Please ignore all earlier ideas in this chat. Based on this version only, what flaws or risks do you see?”

Precision reduces ambiguity, and ambiguity is where hallucinations thrive.

2. Use Narrow-Scope AI Tools

Most hallucinations happen when the AI has too much freedom. So the best tools are the ones that only know what they need to know—and nothing more.

Think of it like hiring a specialist instead of a generalist:

• A good general AI can write a poem, summarize a book, and give vacation tips.

• A focused AI can evaluate a legal document, prep a spreadsheet, or score a job candidate, all because it was built only for that task.

These narrow-purpose bots are far more accurate because they:

• Only use trusted source material

• Are programmed to stay inside clear boundaries

• Avoid making up answers when they hit uncertainty

That’s the difference between a helpful assistant and a hallucination machine.

What This Has to Do with Real Estate Investing

In real estate investing, bad information isn’t just inconvenient. It’s expensive. If your AI gives you the wrong number, a fake comp, or a feel-good answer that hides a serious flaw in the deal, you could lose tens of thousands of dollars. Investors cannot afford to rely on generic chatbots that guess their way through complex transactions. You need tools that actually understand investment strategy, funding requirements, and the hard math behind every property. At The Quantum Funding Company, we don’t treat AI as a novelty. We build it to work—so investors can move with speed, accuracy, and confidence in a business where one wrong answer can kill a good opportunity.

Final Thought

AI is powerful, fast, and surprisingly helpful. But it is not infallible. If you expect perfection, you’ll be disappointed. But if you understand how and why it fails, you can use it more wisely. Ask better questions. Use the right tools. Stay skeptical, and stay sharp.

Because when used well, AI doesn’t replace human judgment. It amplifies it.