Mastering GitHub Copilot: from basics to advanced prompts

Learn basics, define custom instructions, and build complex standalone prompts to make AI work like your second self

What you’ll learn
Use GitHub Copilot features such as Inline Chat, Chat View, Code Completions wit ease.
Use contextual signals (meaningful naming, type declarations) that allow Copilot Code Completions to suggest the most relevant results.
Construct prompts with specific context (open files, specially added files with logic to include in results or use as an example, links to webpages).
Specify rules and guidelines in custom instructions for the Copilot to apply while generating code. Configure custom instructions.
Make Copilot or the AI follow your coding style or work like your second self.
Construct complex standalone prompts for repetitive routine tasks. For example: /add-form, /add-feature, /add-feature-back-end, /add-api-endpoint, etc.

Requirements
Complete beginners for GitHub Copilot
You should know JavaScript, TypeScript, next.js and React
Developers with experience and their own style of code writing.
Junior developers might face difficulties here. While the theory and use cases may be easy to understand, the resulting code itself could be complex to grasp. Although the code shown in the course could be a good starting point for learning project architecture, be aware that the feature-based architecture used here is quite complicated.

Description
This course is a practical guide to making AI to become a very helpful and effective assistant in writing code, or even better, to have it work like your second self.Interaction with the AI occurs via prompts that instruct it on what to do. GitHub Copilot is a versatile tool that can construct prompts automatically (the Code Completions feature) or allow the developer to write them manually in a dedicated interface (Inline Chat, Chat View).Building any application starts with defining guidelines to ensure consistent logic throughout the project. Developers then perform various tasks, ranging from describing data structures and writing simple functions/components to implementing entire features. This course was created from the perspective of building a project by performing these tasks and leveraging AI to automate or speed up many of them.While the principles apply broadly, I use GitHub Copilot as a tool of choice because it provides all the necessary features for constructing and executing prompts.At the beginning, you will learn GitHub Copilot basics. This essential knowledge is taught through practical examples and use cases that you will surely face in your daily practice. Furthermore, lessons include subtle moments to keep in mind to get the most from the AI.In lessons covering Inline Chat and Chat View, you will learn that besides writing the prompt, you can provide extra context by adding files or links to relevant webpages. Copilot then pulls this external data together to construct the final, comprehensive prompt.Later in the course, you will discover how to include specific guidelines for all prompts by declaring them in Custom Instructions. This is the first step in ensuring Copilot begins to follow your coding style. Subsequent lessons will share more secrets about making the AI adhere to your specific style.The course then introduces standalone prompts to handle repetitive tasks. You will learn how to call these complex prompts as a simple command in Inline Chat or Chat View.I have gone a little bit further by creating complex standalone prompts like /add-form, /add-feature, /add-feature-backend, and /add-api-endpoint. Using these, you will be able to scaffold entire logic, split across multiple files and adjusted to the specific data structure and details you provide in the prompt call. This will significantly boost your productivity.I hope this course will inspire you to search for routine tasks that can be automated and write your own standalone prompts for them.

Who this course is for
This course is for developers seeking all possible ways to use AI as effectively as possible, particularly to automate many routine tasks. My goal was to make AI work like my second self. That is, the AI creates a backend feature, implements an API endpoint, generates a frontend feature, and builds a form precisely in the way I like. To achieve this, I identified the most common tasks and wrote dedicated standalone prompts for them.

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