What is Hallucination Detection?
TL;DR
Automated detection of factual errors and fabricated citations in LLM output. The combination of RAG, LLM-as-Judge, and citation verification is now mandatory in production AI systems.
Hallucination Detection: Definition & Explanation
Hallucination Detection is the body of techniques used in production to flag factual errors, fabricated citations, and non-existent APIs in LLM output. By 2024-2026 it is a non-optional component of any production AI system. Methods: (1) Citation Verification (does the cited URL exist? does the cited passage match the source?), (2) Self-Consistency (sample 5-10 times — outliers are likely hallucinations), (3) LLM-as-a-Judge (a second model fact-checks), (4) RAG-based fact-check (retrieve from internal docs or web for verification), (5) Confidence scoring (low-confidence outputs are flagged), (6) Constraint satisfaction (JSON Schema violations, type mismatches), (7) Adversarial probing (loaded questions designed to elicit hallucinations). Key tooling: Vectara HHEM (Hugging Face Enterprise), Patronus Lynx (open-source hallucination detector LLM), Galileo Luna, Phoenix (Arize), Ragas, TruEra. Standard benchmarks: TruthfulQA, HaluEval, SimpleQA, FActScore. Production pipeline pattern: [user query] → [RAG retrieval] → [LLM generation with mandatory citations] → [LLM-as-Judge verification] → [confidence routing] → [low score: human review queue / high score: auto-publish]. In legal, medical, and financial domains, running production AI without hallucination detection is malpractice. With the EU AI Act enforcement timeline, hallucination-rate disclosure is on track to become a legal SLA in 2026 enterprise contracts.