Recursive Deductive Verification: A framework for reducing AI hallucinations
: I've been working on a systematic methodology that significantly improves LLM reliability. The core idea: force verification before conclusion. The Problem: LLMs generate plausible-sounding outputs without verifying premises. They optimize for coherence, not correctness. RDV Principles:
Never assume - If not verifiable, ask or admit uncertainty Decompose recursively - Break complex claims into testable atomic facts Distinguish IS from SHOULD - Separate observation from recommendation Test mechanisms first - Functions over essences, reproducible behavior over speculation Intellectual honesty over comfort - "I don't know" is valid
Practical Results: Applied as system instructions, RDV significantly reduces:
Hallucinations (model stops instead of confabulating) Logical errors (decomposition catches flaws) Unjustified confidence (verification reveals gaps)
Example: Without RDV: "The best solution is X because Y" (unverified assumption) With RDV: "What are we optimizing for? What constraints exist? Let me verify Y before recommending X..." Implementation: Can be added to system prompts or custom instructions. The key is making verification a required step, not optional. This isn't about restricting capability - it's about adding rigor. Better verification = more reliable outputs. Open question: Could verification frameworks like this be built into model training rather than just prompting?
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