Prompt Engineering in Practice: Design, test, and improve AI prompts
English | 2026 | ISBN: 1633436306 | 335 pages | True EPUB | 5.78 MB
Get the eBook free when you register your print book at Manning."A thoughtful, practical guide to prompt engineering as a real discipline."-Che Gamble, Davies GroupSometimes your LLMs return brilliant responses. Other times, not so much. Do you know why?Prompt Engineering in Practiceshows you how to move from accidental AI results to reliable, production-grade systems you can deploy with confidence. Written by AI veteransRichard DaviesandRafael Fischer, this book introduces a unique approach: treat prompts as engineered, self-contained interfaces that you can compose, evaluate, and refine. This shift reframes model interactions as a strict software design discipline rather than a series of fuzzy trial-and-error exercises.Spanning 11 comprehensive chapters,Prompt Engineering in Practiceestablishes a logical "design stack" that builds from microscopic syntax to macroscopic system architecture. You'll start by learning to define the structural elements of a prompt, including delimiters and falsifiable constraints, along with linguistic characteristics like precision, directness, and brevity. These techniques equip you to write well-specified prompts that you can successfully incorporate into reusable production components.You'll then explore the foundational patterns that form the core of prompt engineering and enable you to build robust, scalable agentic workflows. ReviewerDewang Sultania, Senior Machine Learning Engineer at Netflix, noted "The nine-pattern taxonomy provides genuinely useful composable prompt engineering strategies. It's also a great introduction on how LLM-powered systems actually work!"Because LLM responses are consistently inconsistent, the book provides a practical framework to diagnose prompt failures systematically by treating prompts as specifications and investigating sources of variance. Throughout the book, you'll practice a process to systematically analyze and troubleshoot prompt failures as you navigate four distinct phases of the prompt engineering lifecycle-Design, Test, Iterate, Manage. By the time you reach the deployment and operations chapters that conclude the book, you'll be treating unexpected outputs as valuable debuggable system signals that help you isolate where a prompt is underspecified.Ultimately,Prompt Engineering in Practiceshows you how to treat prompts as first-class, maintainable software artifacts. You will learn to eliminate "prompt debt" by building version-controlled, audited prompt libraries that can be reviewed in pull requests, secured against injection, and managed using role-based access controls. By bridging the gap between prototype experimentation and production operations, this guide provides the exact tools needed to collaborate with cross-functional teams and maintain consistent, safe AI integrations over time.What's inside* Eliminate prompt debt with version-controlled, audited prompt libraries* Reduce development iteration and rework through deliberate linguistic precision* Mitigate hallucinations and security vulnerabilities using robust hardening techniques* Diagnose and debug model output failures using systematic engineering frameworksAbout the readerThis book is for software developers and AI engineers who want to build, secure, and maintain reliable LLM-powered systems in production.About the authorRichard Daviesis the Founder, CEO, and CTO of Meridias. He has worked exclusively in artificial intelligence since 2018, with hands-on experience across machine learning, agents, and Large Language Models.Rafael Fischer, PhD in Engineering, specializes in building and delivering agentic LLM solutions with clients in the US, Europe, and Brazil.Table of ContentsPart 1 Foundations1 Prompt engineering: The blueprintPart 2 Prompt Design2 Prompt design: Structural elements3 Prompt design: Linguistic elements4 Patterns5 Templates6 Prompt typesPart 3 Advanced Prompting7 Contextual prompting8 Prompt sampling9 Advanced prompt patternsPart 4 Practice10 Security11 Managing Prompts in Production
Get the eBook free when you register your print book at Manning.
"A thoughtful, practical guide to prompt engineering as a real discipline."-Che Gamble, Davies Group
Sometimes your LLMs return brilliant responses. Other times, not so much. Do you know why?
Prompt Engineering in Practice
shows you how to move from accidental AI results to reliable, production-grade systems you can deploy with confidence. Written by AI veterans
, this book introduces a unique approach: treat prompts as engineered, self-contained interfaces that you can compose, evaluate, and refine. This shift reframes model interactions as a strict software design discipline rather than a series of fuzzy trial-and-error exercises.Spanning 11 comprehensive chapters,
establishes a logical "design stack" that builds from microscopic syntax to macroscopic system architecture. You'll start by learning to define the structural elements of a prompt, including delimiters and falsifiable constraints, along with linguistic characteristics like precision, directness, and brevity. These techniques equip you to write well-specified prompts that you can successfully incorporate into reusable production components.You'll then explore the foundational patterns that form the core of prompt engineering and enable you to build robust, scalable agentic workflows. Reviewer
, Senior Machine Learning Engineer at Netflix, noted "The nine-pattern taxonomy provides genuinely useful composable prompt engineering strategies. It's also a great introduction on how LLM-powered systems actually work!"Because LLM responses are consistently inconsistent, the book provides a practical framework to diagnose prompt failures systematically by treating prompts as specifications and investigating sources of variance. Throughout the book, you'll practice a process to systematically analyze and troubleshoot prompt failures as you navigate four distinct phases of the prompt engineering lifecycle-Design, Test, Iterate, Manage. By the time you reach the deployment and operations chapters that conclude the book, you'll be treating unexpected outputs as valuable debuggable system signals that help you isolate where a prompt is underspecified.Ultimately,
shows you how to treat prompts as first-class, maintainable software artifacts. You will learn to eliminate "prompt debt" by building version-controlled, audited prompt libraries that can be reviewed in pull requests, secured against injection, and managed using role-based access controls. By bridging the gap between prototype experimentation and production operations, this guide provides the exact tools needed to collaborate with cross-functional teams and maintain consistent, safe AI integrations over time.
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