AI Evaluation & Reliability

Model scorecards look precise, but they are easy to misread. This guide explains what LLM benchmarks are, how to read them, when to distrust them, and how to run your own. No prior AI experience required.

AI doesn't fail loudly. It fails gradually, convincingly, and at scale. The failure modes that quietly wreck production systems before anyone notices.

A solid harness makes your agent reliable at launch. A self-improving system keeps it reliable over time. This is the engineering discipline that separates production AI from drifting AI — failure mining, eval generation, regression gating, and the feedback loop architecture that ties it all together.

What 100+ Production Prompts Taught Me About Reliability

Teaming in AI integrates offensive and defensive expertise through multiple specialized teams. Organizations implementing comprehensive teaming detect 92% more vulnerabilities and reduce fix costs by 78%.

AI doesn't create garbage; it recycles your mess at warp speed. How bad data poisons AI at the training and prompting stages, and what you can do about it.

How RAG systems and context engineering can poison your AI, plus the governance layer and action plan to fix data quality across your entire pipeline.