Название: AI Engineering in Practice (MEAP 4)
Автор: Richard Davies, Rafael Fischer
Издательство: Manning Publications
Год: 2026
Страниц: 184
Язык: английский
Формат: pdf (true), epub
Размер: 39.8 MB

Write, refine, organize, and optimize AI prompts that generate relevant and useful text and images!

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.

AI Engineering is Software Engineering that incorporates modern AI techniques, Language Models, Vector Databases, Embeddings, to solve problems involving unstructured data like text, images, and audio. It requires the same discipline as traditional Software Engineering: scalable architecture, systematic testing, robust error handling, and operational monitoring. The distinction lies not in abandoning software engineering fundamentals, but in extending them with AI-specific patterns for problems where traditional approaches fall short.

The capability to process, classify, and extract insights from unstructured data enables solving problems that were previously intractable or required extensive manual effort, making solutions both more capable and more efficient. AI Engineering builds production systems with the modern AI stack, applying architectural patterns that decompose complexity, integration strategies that connect AI to existing systems, validation frameworks that ensure consistent quality, and operational practices that make systems observable and maintainable.

Prompt Engineering provides an interface to Language Models, the techniques for effective communication with AI systems. AI Engineering builds production systems around those interfaces: architectures for reliability, validation for quality control, routing for cost optimization, and operational practices for scalability. Where Prompt Engineering teaches you to communicate with models, AI Engineering teaches you to build production systems with the modern AI stack.

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

Prompt engineering is the discipline of writing instructions for AI models to generate relevant, accurate, and usable completions. AI Engineering in Practice 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.

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.