Khalil Adib

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.

books by Khalil Adib

Prompt Engineering in Practice

  • MEAP began May 2024
  • Last updated May 2026
  • Publication in Fall 2026 (estimated)
  • ISBN 9781633436305
  • 223 pages (estimated)
  • printed in black & white
resources: Book forum

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.