[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-chain-of-verification::en":3,"gloss-cluster-chain-of-verification::en":23,"gloss-next-chain-of-verification::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"chain-of-verification","prompt-eng","Chain-of-Verification","Chain-of-Verification (CoVe) is a hallucination-reduction pattern in which the model drafts an answer, then generates a set of independent verification questions about the claims in that draft, answers each one on its own (without being biased by the draft), and finally produces a revised answer that reconciles any contradictions. Introduced by Meta AI (Dhuliawala et al., 2023), its insight is that models are often better at checking a discrete fact in isolation than at getting everything right in one confident pass. For SaaS builders shipping anything fact-sensitive — summaries of user data, research assistants, support answers — CoVe is a structured self-check that catches fabricated specifics before they reach the user. The trade-off is several extra model calls per answer, so it's best reserved for high-stakes outputs rather than every request. It reduces but does not eliminate hallucination — the verification answers can still be wrong — so for hard accuracy guarantees, pair it with retrieval against a trusted source and human review.","Chain-of-Verification cuts hallucinations by having the model draft, generate verification questions about its own claims, answer them blind, then revise.",null,[11,14,17,20],{"slug":12,"name":13},"grounding","Grounding",{"slug":15,"name":16},"hallucination","Hallucination",{"slug":18,"name":19},"llm-as-judge","LLM-as-Judge",{"slug":21,"name":22},"self-refine","Self-Refine",[24,28,31,34,38,41,44,47,50,53,56,59],{"slug":25,"category":5,"name":26,"updated_at":27},"analogical-prompting","Analogical Prompting","2026-08-24T02:46:37+00:00",{"slug":29,"category":5,"name":30,"updated_at":27},"automatic-prompt-optimization","Automatic Prompt Optimization",{"slug":32,"category":5,"name":33,"updated_at":27},"chain-of-density","Chain of Density (CoD)",{"slug":35,"category":5,"name":36,"updated_at":37},"chain-of-thought-prompting","Chain-of-Thought Prompting","2026-08-24T02:46:36+00:00",{"slug":39,"category":5,"name":40,"updated_at":37},"chunking","Chunking",{"slug":42,"category":5,"name":43,"updated_at":37},"constrained-decoding","Constrained Decoding",{"slug":45,"category":5,"name":46,"updated_at":37},"context-stuffing","Context Stuffing",{"slug":48,"category":5,"name":49,"updated_at":37},"delimiter","Delimiter",{"slug":51,"category":5,"name":52,"updated_at":27},"directional-stimulus-prompting","Directional Stimulus Prompting",{"slug":54,"category":5,"name":55,"updated_at":27},"emotion-prompting","Emotion Prompting",{"slug":57,"category":5,"name":58,"updated_at":37},"few-shot-prompting","Few-Shot Prompting",{"slug":60,"category":5,"name":61,"updated_at":27},"generated-knowledge-prompting","Generated Knowledge Prompting"]