KEY LEARNINGS
- AI hallucinations differ from standard software bugs because the system presents false information with absolute confidence.
- Large Language Models do not access a database of facts; they generate text based on statistical probability and pattern matching.
- The 'capability-reliability gap' means models can perform complex tasks like legal drafting while failing at basic factual accuracy.
- Retrieval-Augmented Generation (RAG) reduces hallucination by grounding the AI's responses in retrieved, trusted documents.
- Governance requires treating all AI outputs as drafts that must be verified, rather than authoritative sources of truth.
- 📄NIST: AI Risk Management Framework Generative AI ProfileNIST guidance on generative AI risks including hallucinations.
- 📄OpenAI: GPT-4 Technical Report (Hallucinations)Technical documentation on GPT-4 hallucination rates.
- 📰ACM Survey of Hallucination in Natural Language GenerationAcademic survey of hallucination research.
- Weiser, B. (2023). Here's What Happens When Your Lawyer Uses ChatGPT. The New York Times.
- Ji, Z., et al. (2023). Survey of Hallucination in Natural Language Generation. ACM Computing Surveys.
- Anthropic. (2024). Claude's Character and Hallucination. Anthropic Documentation.
