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Rebuilding the SDLC for Probabilistic AI: A Practical Guide to AI-Native Software Engineering, LLM Evaluation, and Production Guardrails - Couverture souple
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Rebuilding the SDLC for Probabilistic AI: A Practical Guide to AI-Native Software Engineering, LLM Evaluation, and Production Guardrails (Coding Mastery)
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Rebuilding the SDLC for Probabilistic AI (Paperback)
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Paperback. Etat : new. Paperback. 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 levelBuild context aware data pipelines that ground model outputs in factReplace exact match assertions with statistical evaluation pipelines using bootstrap resamplingScale QA using LLM as a judge techniques, and calibrate those judges properlyMonitor for silent semantic drift in production before your users noticeStructure engineering teams for AI native developmentThis 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9798185250440
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Rebuilding the SDLC for Probabilistic AI
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Rebuilding the SDLC for Probabilistic AI
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Rebuilding the SDLC for Probabilistic AI (Paperback)
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Paperback. Etat : new. Paperback. 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 levelBuild context aware data pipelines that ground model outputs in factReplace exact match assertions with statistical evaluation pipelines using bootstrap resamplingScale QA using LLM as a judge techniques, and calibrate those judges properlyMonitor for silent semantic drift in production before your users noticeStructure engineering teams for AI native developmentThis 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798185250440
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