AI Hallucination Detection

Hallucination is one of the biggest challenges in production AI. The model confidently states something that is entirely made up — a fake citation, a nonexistent API endpoint, or a fabricated statistic. Detection is critical for any application where accuracy matters. Detection approaches include: (1) Grounding verification — compare the model's claims against source documents (used in RAG systems). If the answer contains information not in the retrieved documents, flag it. (2) Self-consistency checks — ask the model the same question multiple times. If answers vary significantly, confidence is low. (3) Confidence scoring — some models provide log probabilities per token. Low-probability tokens may indicate hallucination. (4) Fact-checking models — a second model or knowledge base verifies claims. In production, combine multiple approaches. For RAG systems, check that every claim traces back to a source document and surface citations to users. For code generation, validate output by running it. For factual questions, cross-reference with trusted data sources. The key principle: never trust LLM output for high-stakes decisions without verification.