Fact verification checks LLM-generated claims against retrieved documents or knowledge bases for accuracy. Motivation: LLMs hallucinate - generating plausible but false statements. Verification provides factual grounding. Approaches: Retrieval-based: For each claim, retrieve relevant documents, check if claim supported. Entailment models: NLI classifier determines if retrieved text entails claim (entailment/neutral/contradiction). LLM-as-judge: Use model to compare claim with retrieved evidence. Pipeline: Extract claims from response → retrieve evidence per claim → verify each → flag unsupported claims. Granularity: Sentence-level, entity-level, or full-response verification. Response options: Reject unsupported claims, add caveats, regenerate with constraints. Tools: FactScore, TRUE benchmark, Minicheck, custom NLI pipelines. Challenges: Defining ground truth, handling opinions vs facts, partial support, temporal validity. Production use: Critical for high-stakes domains (medical, legal, financial), news/content generation. Trade-offs: Adds latency and cost, may over-reject valid claims without retrieved support. Essential for trustworthy AI systems.