Meta discovered a technique that makes LLMs 94% more accurate. And it completely destroys everything we thought we knew about prompting. It's called Chain-of-Verification (CoVe). Instead of asking the AI to just answer your prompt, CoVe forces the model to critically interrogate its own brain in a 4-step pipeline: 1. Generate Baseline: The AI writes a quick, rough draft response. 2. Plan Verifications: It scans its own draft and builds a list of factual questions to cross-examine itself. 3. Execute Independently: It answers those questions completely separate from the draft so it doesn't repeat its own bias. 4. Final Revision: It rewrites the entire answer using only the verified facts. Traditional prompting tells the model: "Answer this question." CoVe tells the model: "Answer this, figure out how you might have lied to me, fact-check yourself in secret, and then fix your mistakes." The results are a total paradigm shift: - Factual precision more than doubles on complex data tasks. - Massive reduction in hallucinated entities. - Zero fine-tuning required. - Works across GPT, Claude, and Gemini instantly. The reason it works is almost insultingly simple. LLMs are terrible at generating long, perfectly factual narratives in one shot. But they are incredibly accurate at answering short, targeted verification questions.