Rebuilding the SDLC for Probabilistic AI: A Practical Guide to AI-Native Software Engineering, LLM Evaluation, and Production Guardrails (Coding Mastery)

For sixty years, software engineering ran on one assumption: same input, same output, every time. Large language models broke that promise, and most teams are still pretending they didn't.

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.