LLM Hallucinations: Why AI Makes Things Up

This article is written in English. Our training modules are available in French.

AI confidently tells you a fact. It sounds right. It's detailed and specific. But it's completely made up. This is the problem of AI hallucinations—and if you're using AI, you need to understand it.

What Are Hallucinations?

In AI, a hallucination is when the model generates content that is:

• →Factually incorrect — made-up facts, wrong dates, false claims
• →Nonsensical — logically inconsistent or meaningless
• →Fabricated — invented sources, fake quotes, non-existent people

The term comes from the idea that the AI "sees" something that isn't there.

Why Do LLMs Hallucinate?

1. They're Pattern Matchers, Not Knowledge Bases

LLMs predict the next most likely word based on patterns learned during training. They don't "know" facts—they've learned statistical associations.

When asked something outside their patterns, they generate plausible-sounding text that may not be true.

2. No Real-Time Verification

LLMs can't check if what they're saying is accurate. They have no way to verify facts against the real world while generating responses.

3. Training Data Issues

If training data contains errors, outdated information, or biases, these get baked into the model.

4. Pressure to Respond

LLMs are trained to always generate a response. When they don't know something, they often generate an answer anyway rather than saying "I don't know."

5. Probability vs. Truth

Language models optimize for probability, not truth. The most likely next word isn't always the most accurate.

Examples of Hallucinations

Fabricated Citations

User: Cite sources about X

AI: According to Johnson et al. (2019) published in the 
Journal of AI Research...

Reality: This paper doesn't exist

Invented Facts

User: When was the Eiffel Tower built?

AI: The Eiffel Tower was completed in 1887...

Reality: It was completed in 1889

Non-Existent People

User: Tell me about the CEO of Company X

AI: John Smith has been CEO since 2015 and previously...

Reality: There is no John Smith, or the details are wrong

Why This Matters

For Individuals

If you trust AI output without verification, you may spread misinformation, make bad decisions, or embarrass yourself professionally.

For Businesses

AI hallucinations in customer-facing applications can damage trust, cause legal issues, or lead to costly mistakes.

For Society

Widespread AI use amplifies the spread of hallucinated information, making it harder to distinguish fact from fiction.

How to Reduce Hallucinations

While you can't eliminate hallucinations entirely, you can reduce them:

1. RAG (Retrieval-Augmented Generation)

Ground the AI's responses in actual documents. When the AI has real sources to reference, hallucinations decrease.

2. Ask for Citations

Request sources for claims. While the AI may still fabricate them, it creates accountability and something you can verify.

3. Narrow the Scope

More specific, constrained questions tend to produce more accurate answers than broad, open-ended ones.

4. Cross-Check

Never fully trust AI output for factual claims. Verify important information independently.

5. Self-Consistency

Ask the same question multiple ways. If the answers contradict each other, there's likely a hallucination.

What's Being Done

The AI industry is actively working on solutions:

• →Better training techniques to improve factual grounding
• →RAG systems becoming standard for knowledge-intensive applications
• →Confidence indicators to signal when the AI is uncertain
• →Citation requirements built into model outputs
• →Fact-checking layers in enterprise applications

Key Takeaways

1. →Hallucinations are when AI generates false but plausible content
2. →They happen because LLMs predict patterns, not facts
3. →AI cannot verify its own claims in real-time
4. →RAG and verification are essential for accuracy
5. →Always cross-check important information from AI

Ready to Build Reliable AI Systems?

This article covered the what and why of AI hallucinations. But building systems that users can trust requires deeper techniques.

In our training modules, you'll learn:

• →Module 5: RAG — Ground AI in reliable sources
• →Module 8: Ethics & Security — Build responsible AI systems

→ Explore the Full Program