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. Generative AI models such as ChatGPT, Stable Diffusion, and Gemini can produce amazingly "human-like" news articles, document summaries, images, computer code, and more--if you know how to write effective prompts. This book will teach you the prompt design and authoring skills you need to get useful and relevant responses from AI models, along with advanced prompting techniques for Retrieval Augmented Generation (RAG), building autonomous agents, and data privacy.
Prompt engineering is the discipline of writing instructions for AI models to generate relevant, accurate, and usable completions. This book shows you how to engineer prompts that ensure the outputs of LLMs and other generative AI models exactly match your requirements. You'll learn how to structure your objectives, take advantage of contextual details, and even pick the right model for your task.
AI Engineering in Practice teaches you how to:
- Design prompts that generate accurate and readable responses from LLMs
- Mitigate hallucinations in LLM output
- Domain-aware content generation using RAG
- How AI model design affects your prompts
- Evaluate, optimize, and organize your prompts
About the book AI Engineering in Practice introduces valuable prompt engineering techniques based on industry usage and AI research. You'll learn by exploring real-world cases and examples, from simple tasks like generating formal emails to using LLMs for data annotation, classifying tech support tickets, and building custom chatbots. You'll appreciate author
Richard Davies' explanation of prompt design patterns and templates that you can customize for your own needs. Along the way, you'll discover automated prompting techniques you can use to create autonomous AI agents, and methods for evaluating your own prompts to ensure they're delivering the quality outputs you desire.
About the reader No special skills with AI or machine learning required. Code examples are in Python.
About the author 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.
