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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. Prompt Engineering in Prictice 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
Automated prompting techniques for AI agents
Contextual prompting, prompt sampling, and advanced prompt patterns
Prompt security and managing prompts in production
Apply reusable prompt patterns and templates to real-world tasks
about the reader
No special skills with AI or machine learning required. Code examples are in Python.
about the authors
Richard Davies is the CTO of Vance, an artificial intelligence US-based startup in the business obligations and observance space. With over 6 years of industry experience, he specializes in developing cutting-edge AI products, including real-time semantic segmentation systems, activity detection algorithms, and machine translation platforms.
Rafael Fischer, PhD, is a Generative AI Software Engineer with over 6 years of experience designing and delivering scalable AI-powered products for companies in the US, Europe, and Brazil. He specializes in building full-stack, product-oriented solutions that integrate LLMs, agentic workflows, and secure, cloud-native architectures to create intuitive, high-impact user experiences.
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This book provides a creative ways of using prompt engineering to use Gen AI to get useful results.
The content is very important especially for those who are using LLMs at work and must seek to be efficient with their time and money if requests and responses are tracked by token cost.
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