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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Prompt Engineering in Practice: Design, Test, and Improve AI Prompts

Prompt Engineering in Practice: Design, Test, and Improve AI Prompts

Fischer, Rafael
Davies, Richard

Paperback

Series: In Practice

General Computers

PREORDER - Expected ship date October 27, 2026

ISBN10: 1633436306
ISBN13: 9781633436305
Publisher: Manning Publications
Published: Oct 27 2026
Pages: 225
Weight: 0.59
Language: English
Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Anyone who's tried to build software on top of an LLM knows that prompts are unpredictable, demanding the exact same discipline and systematic approach as the rest of the engineering stack. This book shows you how to design, refine, and manage prompts using proven patterns and practical techniques drawn from real-world AI development.

The book introduces a systemic approach to prompt engineering based on industry usage and AI research. You'll learn how to structure your objectives, take advantage of contextual details, apply systematic prompt patterns, and even pick the right model for your task. The techniques are model-agnostic, require no machine learning background, and are demonstrated through practical prompt examples and real-world scenarios.

You'll appreciate author Richard Davies' explanation of prompt design patterns and templates that you can customize for your own needs. Learn from real-world cases and examples drawn from the kinds of tasks software engineers actually perform, from generating pull request descriptions and incident summaries to using LLMs for data annotation, classifying tech support tickets, and building custom chatbots.

What's inside

- Mitigate hallucinations in LLM output

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