finelybook
Product Engineering with AI
A practical guide to product engineering in an AI native era
Author(s): Addy Osmani ，Hassan Djirdeh (Author)
- Publisher finelybook 出版社: leanpub
- Publication Date 出版日期: 2026
- Edition 版本: 1st
- Language 语言: English
- Print length 页数: 215 pages
Book Description
Product Engineering with AI by Addy Osmani and Hassan Djirdeh is a practical guide for building modern, AI powered digital products, aimed at people who ship software end to end like engineers, product managers, and designers.
Instead of treating AI as a novelty, the book frames it as a new layer in the product stack that changes how teams prototype, implement, test, and iterate. At the heart of the book is a clear definition of product engineering: a mindset that prioritizes user problems, pragmatic delivery, and learning through iteration, often blending engineering with product and design work.
It also explains how newer practices like prompt engineering and vibe coding connect to this role, especially when you are steering models toward outcomes rather than hand writing every line. From there, it zooms out to show how software engineering got here, and why AI is less a replacement than another major abstraction shift.
In the chapter on how AI is reshaping engineering, it describes the augmented developer workflow where speed increases, but the job shifts toward reviewing, steering, and validating output, including new pressures on collaboration and quality. Once the foundations are set, the book gets hands on with the toolchain. It walks through AI powered development platforms like v0.app, Bolt.new, Lovable, and Replit Agent, highlighting how prompt driven scaffolding can turn an idea into a working prototype fast.
It then covers the latest AI code editors and coding agents like Cline, Cursor, Windsurf, and GitHub Copilot, focusing on iteration, refactoring, and delegating multi step changes across a real codebase. A major theme is the move from chatbots to agents: systems that can retrieve context, take actions, and chain steps, using approaches like RAG and agentic workflows.
It also digs into the plumbing that makes this tractable in real products such as MCP for connecting models to tools and A2A for coordinating specialized agents, plus the API ecosystem that turns models into product building blocks. Finally, it pulls everything together into a practical operating playbook: prompt engineering, AI assisted debugging, UX and accessibility improvements, and AI first product strategy.
The book emphasizes future proof skills like writing strong requirements that both humans and AI can execute against, and it treats responsible AI as part of shipping, including ethics and compliance considerations.
