Why AI Hallucinates

AI models predict the most likely next word — not truth. They’re designed to be helpful, not accurate. This fundamental design causes hallucinations.

Hallucination Rates by Model

Model	Hallucination Rate	Notes
GPT-5	~15%	Improved over 4o
Claude 4	~12%	Best among premium
Gemini 2.5	~18%	Higher rate
DeepSeek R1	~20%	Reasoning helps

Techniques to Reduce Hallucinations

1. RAG (Retrieval-Augmented Generation)

Ground answers in your data. The model can only hallucinate what it doesn’t see.

2. Prompt Engineering

Be explicit: “Only answer based on the provided context. Say ‘I don’t know’ if uncertain.”

3. Chain of Verification

Have the AI verify its own output:

“Check your answer for accuracy. List any assumptions.”

4. Temperature Control

Lower temperature (0.1-0.3) = more deterministic, less creative/more accurate.

5. Citation Requirements

Ask for sources:

“Provide citations for each claim.”

What Works Best

In our testing:

• RAG reduces hallucinations by 80%+
• Temperature tuning helps ~20%
• Citation requirements help ~30%

Production Checklist

• Implement RAG for factual queries
• Set low temperature for factual tasks
• Add verification step
• Test with adversarial inputs
• Monitor for hallucinations in production