model-qa

What problem does it solve?

Audits ML and AI models throughout their lifecycle, focusing on LLM evaluation, prompt quality, and governance to ensure safe, reliable outputs.

Core Features & Use Cases

• LLM evaluation framework: standardized prompts, scoring, and reporting for Claude and other models.
• Prompt quality assurance: adversarial testing, calibration checks, and fairness audits with reproducible methodology.
• Governance and documentation: model inventory, methodology replication, and audit-grade reports for compliance and stakeholder review.

Quick Start

Run a structured QA audit on a target model using predefined evaluation prompts and generate a reproducible report.