Why Do LLMs Hallucinate? And How We Can Fix Them in 2025

Philip Moses
4 hours ago
3 min read

Artificial intelligence has become part of everyday life. Tools like ChatGPT, Google Gemini, and other large language models (LLMs) are used for everything from writing emails to researching complex topics.

But there’s one problem that still hasn’t gone away: sometimes, these AI systems confidently give answers that are just plain wrong. This is called an AI hallucination.

In this blog, we’ll break down why LLMs hallucinate and what the AI industry is doing in 2025 to reduce and fix these mistakes.

What Is an AI Hallucination?

An AI hallucination happens when a chatbot creates information that looks right but isn’t true.

For example:

Giving the wrong birthday for a famous person.
Inventing a book or research paper that doesn’t exist.
Making up statistics without real sources.

The key thing to understand is that the AI isn’t “lying.” Instead, it’s predicting text patterns without checking facts.

Why Do LLMs Hallucinate?

There are four main reasons why hallucinations happen:

They guess instead of knowing facts LLMs don’t store facts in a neat database. They predict the “next word,” which means they sometimes invent details.
Training data isn’t perfect Since AI learns from the internet, it picks up outdated info, errors, and even biases.
No built-in fact-checking Once the AI generates text, it doesn’t verify accuracy before showing you the answer.
Prompts can confuse them The way you ask a question matters. Small changes in wording can lead to very different answers.

How Big Is the Problem in 2025?

Things have improved a lot compared to early models:

Older models (like GPT-3) often gave unreliable results.
Today’s models (like GPT-5 and Google Gemini 2.0) make far fewer mistakes. On some tests, hallucinations dropped to just 1–3% when models used real-time data.
But: hallucinations still exist. They’re rare but not gone.

How Are AI Hallucinations Being Fixed?

The AI industry is tackling this problem from multiple angles. Here are the main solutions:

1. Training for Honesty

Newer models are being trained to say “I don’t know” when they’re unsure, instead of guessing.

2. Real-Time Search (RAG)

Retrieval-Augmented Generation (RAG) allows AI to pull information from live databases or the web, grounding answers in facts instead of guesses.

3. Stronger Fact-Checking Tests

Companies like OpenAI and Google now use massive question banks to measure factual accuracy. Gemini 2.0 recently hit 83% accuracy on one major test.

4. Detecting Mistakes Automatically

Tools such as SelfCheckGPT and “semantic entropy” methods can flag when a model might be hallucinating. Some chatbots even warn users: “This answer may not be reliable.”

5. Human Oversight

In sensitive areas like healthcare and law, humans still review AI responses before they’re shared.

Are Regulators Getting Involved?

Yes. Governments are starting to pay attention to AI hallucinations:

In Europe, the new EU AI Act requires companies to manage risks and be transparent about reliability.
In the US, the FTC has warned businesses not to market AI as “always correct.”

This pushes AI companies to be clearer with users and more careful with outputs.

The Bottom Line

As of 2025, AI hallucinations are less common but not fully solved. Models like GPT-5 and Google Gemini 2.0 are more reliable than ever, yet mistakes still happen.

The best solutions combine:

smarter training,
real-time data retrieval,
automated fact-checking,
and human review when needed.

So if your chatbot ever gives you a weird or incorrect answer, remember: it’s not trying to trick you—it’s predicting based on patterns. And thanks to ongoing research, those predictions are becoming smarter, more accurate, and more trustworthy every year.