The EU AI Act tells you what you must do. ISO 42001 and NIST AI RMF tell you how to do it systematically. The practical approach: EU AI Act for legal compliance, ISO 42001 for structured implementation, NIST AI RMF for deeper risk management methodology.
Two fundamental performance levers -- pooling eliminates network/auth overhead per request, and optimization eliminates redundant database queries. Master both to build high-throughput applications.
Measuring what matters: How to evaluate LLM quality across dimensions—accuracy, latency, cost, safety.
Documentation is how governance becomes tangible. Model cards, dataset datasheets, and impact assessments are the artifacts that prove you know what your AI does, where it fails, and what risks it carries.
Before modifying any data file, sequentially append a log record to stable storage -- this makes atomic crash recovery possible without data loss. WAL is the architectural foundation of reliable databases and distributed systems.
Article 50 of the EU AI Act requires that people know when they're talking to AI and when content is AI-generated. For consumer-facing chatbots and AI-powered product content, this is the most immediately actionable compliance requirement.
Horizontal data distribution -- split a large dataset across multiple independent database nodes so no single machine becomes a bottleneck, trading consistency complexity for unlimited write scaling.
Feedback is the mechanism by which people grow. The manager who avoids giving hard feedback because they care about people actually cares more about their own comfort than the person's growth.
Three ways to customize LLMs: Each with different trade-offs in cost, latency, and quality.
The EU AI Act uses a four-tier risk classification to determine what obligations apply. Getting this classification right is the foundation of AI governance -- it determines everything from documentation requirements to whether you need a conformity assessment.