verification-quality-library

What problem does it solve?

Coordinate verification patterns to prevent AI output hallucinations and to enforce accountability across AI-assisted tasks.

Core Features & Use Cases

• Pre-execution expected-output declarations and gate checks to lock requirements upfront
• Post-execution comparisons and audit trails with reference materials to validate results
• Human-in-the-loop review gates to ensure production-ready outputs across code, docs, and data

Quick Start

Before starting any task, specify the expected outputs and required human gates, then proceed.