Rebuilding the SDLC for Probabilistic AI

If you are building AI-powered systems and relying on "it looked fine when I checked it", this book is for you.Rebuilding the SDLC for probabilistic AI is a practical, no-fluff framework for engineering reliable software on top of unpredictable LLMs. Written by an engineer who went from high-frequency trading systems to AI engineering, it covers the guardrails, evaluation methods, and monitoring you actually need once "same input, same output" stops being true.What is inside:Why deterministic testing breaks with LLMs, and what to do about itArchitectural guardrails and context-aware data pipelinesStatistical evals using bootstrap resampling instead of pass or fail assertionsLLM as a judge: scaling, calibration, and debiasingCatching silent model drift in productionHow to structure a team for AI-native engineeringNo hype, no philosophy. Just the framework, the tradeoffs, and the code.

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