Coding with AI: Examples in Python

Generative AI can help with far more than filling in the next line of code. Coding with AI: Examples in Python looks at how developers can use AI across a working software project—from shaping an idea and generating code to testing, debugging, and documentation—while still applying their own judgment to the results.

Jeremy C. Morgan builds the discussion around Python projects and familiar development tools, making the subject concrete rather than leaving it at the level of prompts and possibilities.

Bring AI into the development workflow

The book begins with the foundations: what generative AI and large language models do, why their answers can miss the mark, and how AI-assisted coding differs from traditional code completion. From there, it considers where these tools can fit in a developer’s workflow, including planning, code review, troubleshooting, and documentation.

Learn through projects and practical tools

Across its ten chapters, the book moves from first steps with AI-assisted coding to building applications with AI support. Examples use tools such as GitHub Copilot, ChatGPT, Blackbox AI, Tabnine, and Cursor. The projects cover design and discovery, application code, backend development, user interfaces, and a game-building exercise.

Make prompts—and generated code—work harder

Prompt engineering and test generation receive dedicated attention. The emphasis is not simply on asking an AI tool for code, but on giving it useful context, refining requests, and checking what comes back. That perspective is especially relevant when generated code needs to be understood, tested, and maintained.

For Python developers exploring AI assistance

Python developers looking to incorporate generative AI into everyday software work will find a clear path from core concepts to applied examples. The book is also relevant to developers curious about how these techniques may translate to other programming languages.

Explore Coding with AI: Examples in Python for a grounded look at bringing AI tools into the craft of building software—without handing over the developer’s role in deciding what good code should do.