GitHub Copilot tips and tricks

Summary

GitHub Copilot is an AI-powered coding assistant that enhances productivity by generating context-aware suggestions. To maximize its potential, install the extension in your IDE, such as Visual Studio Code. It excels in drafting repetitive code and can be guided with comments for precise suggestions. Copilot's outputs are modifiable and should be vetted through security and quality checks. It also aids in recalling syntax for various frameworks and supports multiple languages, particularly those with extensive public codebases. Integrating Copilot into your workflow can be seamless, as it adapts to your coding style and provides suggestions as you code.

Takeaways

• 🔧 Install the appropriate extension or plugin for GitHub Copilot, based on your IDE.
• 🛠 For Visual Studio Code, search for Copilot in the extensions and select install.
• 👀 Copilot makes suggestions based on the context from files, packages, code, and comments.
• 📝 Use comments to provide more context if Copilot gives vague suggestions.
• 🔄 Copilot excels at handling repetitive or boilerplate code, such as creating data models.
• 💡 Suggestions from Copilot are just that—suggestions. Modify them to suit your needs.
• 🔍 Run all code, including Copilot's suggestions, through normal security checks and code quality linters.
• 📚 Copilot helps with remembering syntax and frameworks by providing suggestions based on descriptions.
• ⌨️ Cycle through multiple suggestions using keyboard shortcuts or the UI to find the best match.
• 🌐 Copilot works best with languages and frameworks that have a large, publicly available code base.
• 💻 Continue coding as usual; Copilot will provide suggestions naturally as you write code.
• 🤖 With practice, interacting with GitHub Copilot becomes a seamless experience.

Q & A

What is the first step to start using GitHub Copilot?

The first step is to install the appropriate extension or plugin for your IDE. For Visual Studio Code, you go to the extensions, search for 'Copilot', and then select install.

How does GitHub Copilot make suggestions?

Copilot makes suggestions based on the context it sees, including the files you've opened, interacted with, the installed packages and libraries, the code you're writing, and the comments you add.

Why might GitHub Copilot give generic or vague suggestions?

Copilot may give generic or vague suggestions if it doesn't have enough context. Adding comments can help provide more context and guide it in the right direction.

In what scenarios does GitHub Copilot excel?

GitHub Copilot excels when dealing with repetitive or boilerplate code, such as creating a data model, where you might otherwise find yourself copying and pasting from elsewhere.

How can you ensure the suggestions from GitHub Copilot are correct?

Remember that suggestions from Copilot are just that—suggestions. You should modify them as needed and run them through the normal processes, including security checks and code quality linters.

What is one way to use GitHub Copilot for dealing with unfamiliar syntax?

You can describe your scenario in natural language, such as what you're looking to validate with a regular expression, and Copilot will suggest the syntax for you.

How can you cycle through multiple suggestions provided by GitHub Copilot?

You can cycle through multiple suggestions by using keyboard shortcuts or the UI and then accept the one that most closely matches what you're trying to do.

When is GitHub Copilot most effective?

Copilot is most effective when working with languages and frameworks where there's a large publicly available code base. It may not be as effective for more obscure languages or those with less publicly available code.

Does using GitHub Copilot require changing the way you code?

No, using Copilot does not mean you need to change the way you code. You can continue writing code as you normally would, and Copilot will make suggestions as you add functions, methods, classes, etc.

How can GitHub Copilot assist with staying in the zone while coding?

Copilot allows you to describe scenarios and get suggestions without having to leave your IDE to look up syntax or search for information, helping you stay focused and in the zone.

What is the role of GitHub Copilot described in the script?

GitHub Copilot is described as an AI pair programmer that assists developers by making suggestions based on the context and the code they are working on.

Outlines

00:00

🤖 Getting Started with GitHub Copilot

This paragraph introduces the process of utilizing GitHub Copilot, starting with the installation of the necessary extension or plugin in Visual Studio Code. It emphasizes the importance of providing context for Copilot to make accurate suggestions, such as through comments or code snippets. The script also touches on the tool's proficiency in handling repetitive code and the necessity of treating Copilot's suggestions as a starting point that may require further refinement and security checks.

Keywords

💡GitHub Copilot

GitHub Copilot is an AI-powered coding companion that assists developers by generating code suggestions based on the context of the code they are working on. It is integral to the video's theme as it is the main subject being discussed. The script mentions installing the extension for it in Visual Studio Code and how it can help with writing code by making suggestions.

💡IDE (Integrated Development Environment)

An Integrated Development Environment is a software application that provides comprehensive facilities to computer programmers for software development. The video script refers to the installation of the GitHub Copilot extension in an IDE, specifically Visual Studio Code, which is a popular choice among developers.

💡Extension

In the context of software development, an extension is a software module that adds functionality to an existing application. The script instructs how to install the GitHub Copilot extension in an IDE to enhance the coding experience by providing AI-generated code suggestions.

💡Context

In the script, context refers to the information that GitHub Copilot uses to make informed suggestions, such as the files opened, the code being written, and comments added by the developer. Understanding context is crucial for the tool to provide relevant and accurate code suggestions.

💡Boilerplate Code

Boilerplate code is a section of code that is repeated in many places with little or no variation. The video discusses how GitHub Copilot excels at generating boilerplate code, which can save developers time and effort by reducing the need to copy and paste from other sources.

💡Suggestions

Suggestions in the video refer to the proposed code snippets generated by GitHub Copilot based on the context it has analyzed. The script emphasizes that these are not final solutions but starting points that developers can modify to fit their specific needs.

💡Security Checks

Security checks are processes to ensure that the code is safe from vulnerabilities and does not introduce security risks. The script advises running any code, including suggestions from Copilot, through security checks as part of the standard coding process.

💡Code Quality Linters

A linter is a tool that analyzes source code to flag programming errors, bugs, stylistic errors, and suspicious constructs. The video mentions the importance of running code through linters to maintain code quality, including code suggested by GitHub Copilot.

💡Frameworks

In software development, a framework is a set of conventions and components that can be combined to create a software application. The script notes that GitHub Copilot can provide suggestions for various frameworks, helping developers who may not remember specific syntax or usage.

💡Regular Expression

A regular expression is a sequence of characters that specifies a search pattern, mainly for use in pattern matching with strings, in the context of string searching algorithms. The video gives an example of how GitHub Copilot can suggest a regular expression based on a natural language description provided by the developer.

💡Publicly Available Code Base

The publicly available code base refers to the collection of code that is accessible to the public, often found in repositories like GitHub. The script mentions that GitHub Copilot performs better with languages and frameworks that have a large publicly available code base, as it uses this data to generate suggestions.

💡AI Pair Programmer

The term 'AI Pair Programmer' is used in the script to personify GitHub Copilot as a collaborative partner in the coding process. It suggests that developers can interact with Copilot naturally, as if it were a human partner, to enhance their coding workflow.

Highlights

Install the appropriate extension or plugin for GitHub Copilot depending on the IDE you are using.

Copilot makes suggestions based on the context of the files, packages, libraries, and code you are working with.

Add comments to provide more context for Copilot to give more accurate suggestions.

Copilot excels at dealing with repetitive or boilerplate code, such as creating data models.

Accept suggestions from Copilot but modify them as necessary to ensure they meet your expectations.

Run all code, including Copilot's suggestions, through security checks and code quality linters.

Use Copilot to get suggestions for syntax you may not remember, such as regular expressions.

Stay in your development zone without needing to search externally for syntax by using Copilot's natural language descriptions.

Cycle through multiple suggestions provided by Copilot using keyboard shortcuts or the UI.

Copilot works best with languages and frameworks that have a large publicly available code base.

Continue coding as you normally would, and Copilot will provide suggestions as you add functions, methods, and classes.

Practice using Copilot to interact naturally with the AI pair programmer after a short period of time.

GitHub Copilot can be a valuable tool for developers to enhance their coding efficiency and accuracy.

Copilot's suggestions are based on the current context and can be refined with additional comments.

The quality of Copilot's suggestions may vary for more obscure languages or those with less publicly available code.

Use GitHub Copilot as an AI pair programmer to assist in your coding process without changing your coding style.

Transcripts

Now that you've been granted access to

GitHub Copilot, let's explore how to get

the most out of the tool. The first step

is to install the appropriate extension

or plugin. The exact steps for

this will vary depending on the

IDE that you're using, but in the case

of Visual Studio Code, you'll go to the

extensions, do a search for Copilot, and

then select install.

Copilot makes suggestions based on the

context that it sees. This includes the

files that you've opened and interacted

with, the packages and libraries that

have been installed, the code that

you're writing, and comments that you add.

If you notice that Copilot is

giving you relatively generic or vague

suggestions, this is typically because

it just needs a little bit more context.

You can use a comment, for example

to help point it in the right direction and

describe exactly what it is that you're

looking for. Copilot that really

shines when it comes to dealing

with repetitive or boilerplate code.

When we think, for example, about

creating a data model, this is

relatively repetitive and typically you

would find yourself copying and pasting

from somewhere else. With Copilot, I can

describe with a comment what I need and

then get a suggestion for a

class that matches the description

that I provided.

Always remember that the suggestions

from Copilot are exactly that,

they're suggestions. This means that even if

you accept them, you're allowed to modify

them to ensure that they are setup

and going to run the way that you expect.

Additionally, you want

to run any code, be it code that

you've created, or code that Copilot has

suggested, through the normal

processes that you would with any code,

and this includes running it through

security checks, code quality linters, etc.

As a developer

there's a lot of different

frameworks and bits of syntax that,

well, we don't always quite remember,

and with Copilot I can describe

my scenario and get a suggestion.

So let's take a regular expression.

I can describe in natural language

what it is that I'm looking to validate, and

and Copilot will then make a suggestion for

me, and this allows me to

avoid having to leave to go look up what

that syntax might be, do a search, or otherwise.

I can stay in the zone and in my IDE

Depending on the

scenario, Copilot will provide multiple suggestions.

You can cycle through these

either by utilizing keyboard shortcuts

or the UI, and then accept the one

that most closely matches what it is

that you're trying to do.

Copilot is at its best when

working with languages and frameworks

where there's a large publicly available

code base. You may find that the

suggestions aren't necessarily to the

quality that you might want for

languages that are more obscure,

or where that level of a

publicly available code base

doesn't exist.

Using Copilot does not mean that you

need to completely change the way that

you code. You can use

comments to describe your scenario and

get suggestions, and you can also just

keep writing code like you normally

would that as you add in functions,

as you add in methods, as you add in classes

as you write code,

Copilot will be making suggestions.

And you will find

that after a little while, after

a little bit of practice, that you'll be

able to interact very

naturally with GitHub Copilot,

your AI pair programmer.