Automation of routine tasks, code generation, fast debugging, and even project design - artificial intelligence is becoming an essential part of a Python developer’s daily work. The book "Learn AI-assisted Python Programming" by Daniel Zingaro is a practical guide to collaborative development with AI, focusing on real-world use of tools like GitHub Copilot, ChatGPT, Tabnine, and others.
The author shows how to use neural networks not for experimentation, but as a solid part of a professional workflow. This is not another theoretical attempt to “explain how AI works” - it’s a manual for writing code faster, better, and smarter. Download the book "Learn AI-assisted Python Programming", second edition in PDF for free and start treating AI as your assistant, not a replacement.
Who Should Read This Guide on AI-Powered Python?
This book is intended for developers who work with or are planning to work with Python and want to efficiently integrate AI tools into their daily workflow. Among them:
- Beginner Python developers will learn how to use AI correctly to receive accurate assistance rather than “magic answers”.
- Experienced engineers will master advanced use cases for AI in large projects and pipelines.
- Mentors and educators will find structured methods to teach students how to work with modern AI tools.
- Automation and QA engineers will learn to generate tests, coverage, and templates faster and with fewer bugs.
How Does "Learn AI-assisted Python Programming", Second Edition Differ From Other Python Books?
- Realistic, engineering-focused approach. Daniel Zingaro doesn’t glorify AI. He explains where it helps, where it fails, and how to handle both.
- Python-centered use. It demonstrates how AI tools enhance Python development - from writing functions and classes to tests and documentation.
- Step-by-step use cases. The book guides you through generating APIs, fixing bugs, optimizing code, and explains why each approach works.
- Tool-agnostic strategy. It covers GitHub Copilot, ChatGPT, Tabnine, Amazon CodeWhisperer, and open-source alternatives - so you can choose what suits you best.
- Understanding risks and limits. You'll learn how to handle AI hallucinations, format output properly, and avoid losing critical thinking skills.
How Can You Apply the Knowledge in Practice?
This book is built around real-world scenarios and offers skills you can use immediately. After reading, you'll be able to:
- Use AI to generate functions, tests, and boilerplate code
- Boost your productivity by crafting effective prompts for AI tools
- Quickly identify and fix bugs in Python scripts
- Automate repetitive tasks using AI assistants
- Improve code readability and structure with AI-driven suggestions
More About the Author of the Book
The Developer's Opinion About the Book
As a developer who actively uses ChatGPT and Copilot, I often see colleagues either overestimating AI or hesitating to try it. This book clearly shows that AI is a tool, not magic. What I appreciate most about Zingaro’s approach is the structure: how to formulate a prompt, verify output, and refine code - it all makes sense and is highly actionable. Even as a seasoned engineer, I discovered several effective strategies for chained generation and test templates. One downside: I’d love to see more about async/await and working with external APIs. Still, it’s a hands-on guide every Python developer should keep close - practical, realistic, and written by someone who’s clearly been in the trenches.
Sarah Bennett, Machine Learning Developer
FAQ for "Learn AI-assisted Python Programming"
Is this book suitable for those just starting with Python?
Yes, but with a caveat. The core material is tailored for developers already familiar with Python basics. However, AI tools can also help beginners: the examples discussed in the book can serve as templates and inspiration. The author explains how to interact with LLMs to generate simple solutions. This is especially helpful for newcomers - you learn to write code and analyze its logic at the same time, gaining hands-on skills from the beginning.
Does the book cover specific tools like GitHub Copilot or ChatGPT?
Yes. It includes practical examples using Copilot, ChatGPT, Tabnine, and more. The author walks you through crafting effective prompts, interpreting responses, and understanding the limitations of each platform. You'll see where one tool works better than another and how to choose the best assistant for each task - from writing functions to designing interfaces.
Will I learn how to write prompts effectively?
Absolutely. Prompt engineering is one of the book’s core topics. The author teaches prompt patterns, how to handle poor outputs, and how to refine requests for better results. This is particularly useful for developers aiming to reduce the time needed to get accurate code. You’ll learn how to move from vague instructions to structured prompts that produce clean, optimized, and task-specific output.
Does the book address trust and reliability of AI-generated results?
Yes. Daniel Zingaro dedicates a full section to the issue of AI “hallucinations” and shares techniques for verification - from manual checks to automated tests. You’ll learn to maintain control over AI output rather than blindly trust it. The book also covers prompt clarification and how to embed validation into your workflow, which is especially useful in CI/CD and when generating production-level functions.
Can this book be used in team or enterprise development?
Definitely. The author provides strategies for integrating AI into professional development workflows - including code review, mock generation, documentation assistance, and even dev - QA collaboration through AI tools. He outlines how responsibilities can be split: LLMs handle repetitive or assistive tasks, while developers maintain control, adaptation, and testing - a model highly relevant in real-world teams.
Do I need to understand machine learning to benefit from this book?
No. The book does not require any understanding of AI algorithms. It focuses on using existing tools, not building them. You’ll learn how to use AI as a development assistant - not as a subject of research. That makes the book accessible and valuable for any Python developer, regardless of their experience with machine learning.
Information
| Author: | Leo Porter, Daniel Zingaro | Language: | English |
| Publisher: | Manning | ISBN-13: | 978-1633435995 |
| Publication Date: | October 29, 2024 | ISBN-10: | 1633435997 |
| Print Length: | 336 pages | Category: | Machine Learning and Artificial Intelligence Books |
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