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The GitHub Copilot Handbook

The GitHub Copilot Handbook

By : Rob Bos, Randy Pagels
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The GitHub Copilot Handbook

The GitHub Copilot Handbook

By: Rob Bos, Randy Pagels

Overview of this book

Cross-functional product teams are under constant pressure to build and ship faster, but too much time is lost to manual coding, slow reviews, and fragmented workflows. GitHub Copilot streamlines day-to-day coding so your team can focus on delivering value to users while maintaining high quality on real projects. Written by industry experts Rob Bos and Randy Pagels, this book shows how GitHub Copilot supports your work from start to finish. You’ll learn how to turn ideas into tasks, write code with fewer hiccups, spot problems earlier, and understand errors when things go wrong. You’ll also see how Copilot suggests improvements in pull requests and helps fix common build issues, enabling teams to keep moving and ship with confidence. You’ll integrate GitHub Copilot into daily routines, share it across roles, and make it stick with simple checklists and clear examples. You’ll also track what works, set guardrails, and build an internal community. By the end, you’ll know when GitHub Copilot helps – and when it doesn’t – and you’ll be ready to write, review, and ship code with confidence on real projects.
Table of Contents (18 chapters)
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1
What is GitHub Copilot?
5
Getting Started with GitHub Copilot
8
Exploring GitHub Copilot Integrations
11
Getting the Most Out of GitHub Copilot
16
Other Books You May Enjoy
17
Index

Ethical use of GitHub Copilot

When using tools that generate parts of your code base, we always need to keep in mind several ethical aspects. There is an aspect of code generation itself where large language models display certain traits based on the way they have been trained. We already mentioned things such as bias in Chapter 2. Another aspect is how engineers use these tools and are diligent in the way the tools impact their way of working. If the engineer stops thinking and blindly accepts every output of the large language models, did they actually gain anything? We don’t think so. This aspect is more of a cultural effect that AI can have on how teams work: we need to refrain from just pointing at the tools and saying it was generated by AI and therefore it should just work. We are the human in the loop, and we need to bring our professional view to creating the business value for our end user.

As engineers, that means we need to stay on top of things and be the human...

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The GitHub Copilot Handbook
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