In this episode of Lunchtime BABLing, BABL AI CEO Dr. Shea Brown dives deep into one of the most pressing questions in AI governance today: how do we ensure the safety of Large Language Models (LLMs)? With new regulations like the EU AI Act, Colorado’s AI law, and emerging state-level requirements in places like California and New York, organizations developing or deploying LLM-powered systems face increasing pressure to evaluate risk, ensure compliance, and document everything. 🎯 What you'll learn: Why evaluations are essential for mitigating risk and supporting compliance How to adopt a socio-technical mindset and think in terms of parameter spaces What auditors (like BABL AI) look for when assessing LLM-powered systems A practical, first-principles approach to building and documenting LLM test suites How to connect risk assessments to specific LLM behaviors and evaluations The importance of contextualizing evaluations to your use case—not just relying on generic benchmarks Shea also introduces BABL AI’s CIDA framework (Context, Input, Decision, Action) and shows how it forms the foundation for meaningful risk analysis and test coverage. Whether you're an AI developer, auditor, policymaker, or just trying to keep up with fast-moving AI regulations, this episode is packed with insights you can use right now. 📌 Don’t wait for a perfect standard to tell you what to do—learn how to build a solid, use-case-driven evaluation strategy today.