AI Articles

Practical AI patterns for trustworthy operational systems.

A guided collection on evidence-backed AI, governed workflows, durable memory, operational context, and product systems that can explain and verify their own behavior.

Recommended reading path

1. The danger is not that AI sounds wrong. The danger is that it sounds right.Why fluent AI answers still need evidence, judgment, and verification.2. Three kinds of AI people often mix togetherHow predictive AI, generative AI, and conversational AI solve different problems and need different boundaries.3. Governed AI workflowsHow meaningful actions move through staging, validation, approval, execution, and audit.4. Source-backed recommendationsHow useful guidance separates evidence, assumptions, constraints, caveats, and suggested next steps.5. Durable operational memoryHow decisions, evidence, outcomes, and lessons can improve future guidance when memory stays bounded and inspectable.6. Operations intelligence packsHow curated context gives AI and operators useful background before the moment of pressure.7. Operational intelligence for crisis workflowsHow systems can suggest the next responsible step without taking authority away from the operator.8. Self-demonstrating systemsHow demos, onboarding, training, and regression checks can share the same product runtime.9. Training and regression testing should share the same runtimeWhy product demos, onboarding, training, and regression tests should exercise the same behavior.

Evidence-Backed AI

The Evidence-Backed AI hub collects the operating principles behind this work: sources, boundaries, permissions, freshness, caveats, review, and responsible use of automation.