Why does AI Hallucinate? (And How to Reduce It)

The AI didn’t make up facts.
It just accessed the parallel universe where your podcast got 10 million downloads… 
and Kevin Hart endorsed it.
We live in the wrong timeline.

Ever ask ChatGPT a simple question only to receive a confident, yet completely incorrect, answer? I have often requested quotations by famous writers for my newsletter, only to receive eloquent, yet fabricated, quotations. That is what we call an AI hallucination.

AI is powerful, creative, and fast. But it is not always reliable. If you do not understand why these mistakes happen, you risk mistaking fiction for fact. And in 2025, while hallucinations are less frequent thanks to advances like Deep Research, they have not gone away.

To use AI well, you need to know why hallucinations happen, how they show up, and what you can do to reduce them.

What Is an AI Hallucination?

An AI hallucination occurs when a model, such as ChatGPT, Claude, or Gemini, generates something that appears accurate but is factually incorrect. It is the equivalent of a confident student who does not know the answer to a question and so makes something up that sounds plausible.

Common examples include:

Fake citations (where AI invented books or articles that do not exist)

Case: When asked about U.S. legal precedents, ChatGPT 3.5 produced non-existent court cases, such as “Martinez v. Smith, 2012”, and provided made-up docket numbers. This incident was widely reported after lawyers submitted hallucinated cases in a court filing.

Case: In academia, users asking for references often receive real-sounding book titles or journal articles that do not exist (e.g., “Johnson, A. (2017). Cognitive Models in AI. Oxford University Press”).

Made-up statistics or numbers

Case: AI sometimes fabricates statistics with exact percentages. For example, in 2023, ChatGPT claimed “72% of small businesses that adopt AI see revenue growth in the first year” — a number that looked precise but had no source.

Case: Claude and other LLMs have also invented year-specific numbers when asked about market size, e.g., “The AI market was worth $543 billion in 2024” when no such figure existed in any real report.

Make false claims with absolute confidence.

Case: When asked in early 2024 if Elon Musk had stepped down from Tesla, some models confidently asserted “Yes, he resigned in March 2024”, even though that never happened.

Case: Perplexity (before refinements) occasionally misattributed quotes, saying “Einstein said X” with complete confidence, even though historians confirm no such record exists.

It feels real because the AI is fluent in language. But fluency is not the same as truth.

Why Do AIs Hallucinate?

At the core, hallucinations happen because AI (Large Language Models) predicts language.

Predictive text: LLMs generate the most likely next word based on patterns in their training data. That does not guarantee accuracy.

No built-in “I don’t know”: Traditional LLMs prefer giving something over admitting ignorance.

Data gaps: If training data is incomplete or outdated, the model improvises.

Probability over truth: The most likely next word is not always the correct one.

The result: confident-sounding answers that may be entirely fictional.

Are Hallucinations Still a Problem in 2025?

Yes. Though the picture has improved somewhat.

Reduced, not eliminated: GPT-5, Claude 3.5, and Gemini 2.5 are far less likely to hallucinate than their 2023 predecessors. But mistakes still occur.

Deep Research and RAG (Retrieval-Augmented Generation): Agentic browsing and retrieval features can consult and cite live sources before responding, which reduces hallucinations.

Edge cases remain: Niche topics, multi-step reasoning, and creative generation are still prone to hallucinations.

Dual nature: In factual work, hallucinations are a flaw. In creative writing, they can be a feature that generates fresh, imaginative material.

The Consequences of AI Hallucinations

AI hallucinations are not just quirky mistakes. They have real consequences.

Misleading information: Wrong facts in health, finance, or research can be dangerous.

Erosion of trust: If users discover AI makes things up, they lose confidence in its output.

Wasted time: Chasing false references or correcting errors eats into productivity.

Creative upside: On the other hand, hallucinations can be beneficial in fiction, storytelling, or brainstorming, where accuracy is not the primary goal.

How to Reduce AI Hallucinations

You cannot eliminate hallucinations. But you can reduce the risk and manage them wisely.

Here are practical steps for everyday users:

1. Ask for sources. Always request citations or links. If AI cannot provide the answer, double-check it before trusting it.
2. Use hybrid tools. ChatGPT, combined with browsing, Gemini Deep Research, and Perplexity (which displays sources by default), integrates retrieval with generation for enhanced accuracy.
3. Cross-check with search engines. When facts matter, run a quick Google or Bing check.
4. Refine your prompts. Be specific about the format, domain, or level of detail you need.
5. Keep humans in the loop. Apply your judgment. Do not outsource truth entirely to AI.

FAQ About AI Hallucinations

Q: Why does AI make things up when it does not know the answer?

A: Because it predicts words, not facts. If data is missing, it fills the gap with the most likely guess.

Q: What exactly is an “AI hallucination” in ChatGPT or Gemini?

A: It is when the AI generates fluent but false information, such as invented sources or statistics.

Q: Are hallucinations still a big problem with GPT-5 and Claude 3.5?

A: Less than before, but still present. Especially in niche or complex queries.

Q: Can Deep Research or RAG completely solve AI hallucinations?

A: Not yet. Some AI can reduce errors by retrieving real data, but no system is perfect.

Q: What is the difference between a human mistake and an AI hallucination?

A: A human usually knows when they are guessing. AI does not. It presents guesses as facts.

Q: How can I tell if an AI answer is wrong or fabricated?

A: Check for citations. If a claim cannot be verified as originating from a credible source, treat it with scepticism.

Q: Is it safe to use AI if it sometimes hallucinates facts?

A: Yes, if you use it responsibly. AI is a co-pilot, not a replacement for judgment.

Q: What are the best tools in 2025 for reducing hallucinations?

A: Perplexity, ChatGPT with browsing, and Gemini Deep Research.

Q: How can I quickly fact-check AI responses?

A: Cross-check key claims with a search engine or ask the AI to show its sources.

Q: Are hallucinations always bad, or can they be useful for creativity?

A: They are bad for facts, but good for brainstorming. Treat them as a feature when imagination matters.

Key Takeaways

• Hallucinations happen because AI generates language, not facts.
• Even in 2025, they are reduced but not eliminated.
• Tools like Deep Research and RAG are significant improvements, but human verification is still essential.
• Hallucinations are flaws in factual contexts, but features in creative work.

Conclusion

AI hallucinations are not a bug. They are the natural result of how large language models work.

The good news is that with today’s tools and some innovative practices, you can minimise the risks and maximise the benefits. Use AI for what it is good at, verify when accuracy matters, and embrace its creativity when you want inspiration.

👉 Want more practical strategies for mastering AI? Subscribe to The Intelligent Playbook for guides that help you get results without falling for AI’s blind spots.