For sixty years, software engineering ran on one assumption: same input, same output, every time. That assumption is what made unit tests, CI/CD, and Friday afternoon deploys possible. Large language models broke it. They do not calculate, they predict, and that single shift is quietly wrecking how most teams build, test, and ship AI powered systems.
Rebuilding the SDLC for Probabilistic AI is a practical guide for senior engineers, architects, and team leads who need to build reliable software on top of fundamentally unreliable components. Author Sujal Choudhari, who moved from ultra low latency C++ trading systems into AI engineering, walks through why manual vibe checks and eyeballing outputs do not scale, and what to build instead.
Inside, you will learn how to:
Design architectural guardrails that enforce structure at the token level
Build context aware data pipelines that ground model outputs in fact
Replace exact match assertions with statistical evaluation pipelines using bootstrap resampling
Scale QA using LLM as a judge techniques, and calibrate those judges properly
Monitor for silent semantic drift in production before your users notice
Structure engineering teams for AI native development
This is not management fluff or AI hype. It is a concrete, opinionated engineering framework for anyone tasked with shipping AI powered systems that actually hold up in production.