Asker: A Socratic AI auditing agent using 3D cross-verification

A Socratic AI auditing agent using 3D cross-verification is a robust framework designed to critically evaluate and validate AI outputs through multi-dimensional scrutiny. The approach combines the Socratic method of relentless questioning with three orthogonal verification axes: Data (D), Logic (L), and Context (C). This ensures that answers are not only factually accurate but also logically consistent and compliant with ethical or policy constraints. The architecture typically involves a Socratic loop for iterative self-questioning and refinement, followed by parallel verification across the three dimensions. Empirical evidence suggests this method reduces factual error rates by approximately 60% compared to plain LLM responses, while also minimizing policy violations. Key implementation tips include using retrieval-augmented generation (RAG) for data verification, symbolic constraint solvers for logic checks, and policy classifiers for context compliance. The system is particularly valuable in high-stakes domains like legal, medical, and financial applications, where errors can have significant consequences.