# GitHub Copilot: The World's Most Widely Adopted AI Developer Tool

Dave Bernes, GitHub | AI Engineer World's Fair 2024 | 29:49

Source: https://www.youtube.com/watch?v=uhoPM-ABuV0
Channel: AI Engineer (https://www.youtube.com/@aiDotEngineer). Summarised by AIE Talks.
Page: https://aietalks.com/talks/github-copilot-the-worlds-most-widely-adopted-ai-developer-tool
Published: 2025-02-06
Tags: agents, code-generation, coding-agents, developer-experience, rag

## TL;DR
- GitHub Copilot has expanded from inline code generation to chat, code explanation, test generation, bug fixes, pull request summaries, and organization-specific answers.
- Copilot suggestions still need a developer to review them, as shown when it initially allowed speakers to be deleted while talks still existed.
- Copilot Enterprise connects repository context, knowledge bases, web search, pull requests, and external services so developers can work from GitHub without changing tools.

## Summary
Dave Bernes gives a tour of GitHub Copilot's capabilities across the IDE and GitHub.com. He starts with inline code generation, showing how natural-language descriptions can produce Django model classes and regular expressions. He then demonstrates Copilot Chat explaining unfamiliar code, refactoring it, proposing bug fixes, and generating unit tests. The examples also show why developers must review suggestions and provide precise context. Bernes moves on to Copilot extensions, including an Octopus Deploy integration that reports deployment status from chat. Copilot Enterprise adds repository indexing, organization knowledge bases, web search, and pull request features. In the demo, it finds sales-tax code across a repository, suggests public APIs, and warns against hardcoding API keys. It can also write pull request summaries and explain individual changes. Bernes briefly shows that Copilot can explain COBOL and help translate it into other languages, then points attendees to training resources and a Copilot Workspace demonstration at the GitHub booth.

## Key ideas
### Copilot has grown from inline code generation into a broader developer assistant
[00:39](https://www.youtube.com/watch?v=uhoPM-ABuV0&t=39s)
Bernes describes the product's progression from Copilot Individual generating code in an IDE, to Copilot Business, Copilot Chat, and Copilot Enterprise. Chat can explain existing code, simplify it, and help with other programming tasks. Enterprise brings Copilot to github.com, where it can summarize pull requests and answer questions using organizational knowledge bases. The tool runs in Visual Studio Code, Codespaces, JetBrains IDEs, and Visual Studio. The source code can live on GitHub, Bitbucket, Azure DevOps, or even Team Foundation Version Control.

### Natural-language prompts can generate useful code while keeping developers in the IDE
[03:12](https://www.youtube.com/watch?v=uhoPM-ABuV0&t=192s)
In a Django example, Bernes describes model classes in natural language and Copilot generates the class definitions across multiple lines. It notices the existing Speaker class and generates a related Talk class. The developer then asks for a change to the deletion behavior and describes a code format as three letters, a dash, and three numbers. Copilot produces the corresponding regular expression. Bernes says this avoids leaving the IDE to search Stack Overflow or documentation for syntax.

### Copilot needs developer review because its first suggestion can be wrong
[05:07](https://www.youtube.com/watch?v=uhoPM-ABuV0&t=307s)
The Django example initially allowed speakers and talks to be deleted even when talks still existed. The developer noticed the problem, added a more specific request, and accepted a revised suggestion. Bernes uses this to explain why Copilot is not an autopilot and why a person remains responsible for checking generated code. He also connects the example to prompt crafting: specific comments and requests give Copilot better context and more accurate suggestions.

### Copilot Chat can explain, refactor, debug, and test code in context
[07:51](https://www.youtube.com/watch?v=uhoPM-ABuV0&t=471s)
Bernes demonstrates Chat on inherited code containing poorly documented regular expressions. Copilot explains that the expressions validate email addresses, phone numbers, and strong passwords. It then separates the validation functions, adds comments, and improves variable names. In a broken program, Copilot notices that the code treats data as comma-separated even though the comment says it is space-separated. It also catches a return-order mismatch and adds error checking. From the same code and comments, it generates unit tests covering the logic branches.

### Extensions bring deployment information into the coding conversation
[11:01](https://www.youtube.com/watch?v=uhoPM-ABuV0&t=661s)
Bernes introduces Copilot extensions from partners such as MongoDB, Octopus Deploy, and DataStax. In the Octopus Deploy example, a developer changes a Node.js application, pushes the change, and tracks the resulting build and deployment through Copilot Chat. The extension provides a text-based dashboard with deployment status across environments. It can show whether the latest version has reached production and expose useful information from deployment logs, such as URLs. The purpose is to let developers query those systems without leaving the tools where they write code.

### Enterprise uses repository indexing and knowledge bases to answer organization-specific questions
[14:31](https://www.youtube.com/watch?v=uhoPM-ABuV0&t=871s)
Copilot Enterprise adds chat on github.com, organization knowledge bases, and repository search. Bernes explains that knowledge bases are collections of Markdown files stored in repositories. When he asks a generic question about feature flags, Copilot gives general advice. When he adds GitHub Engineering documentation as context, the answer becomes specific and mentions the Flipper framework used internally. In another environment, Enterprise-managed users provide a private, controlled setting for internal repositories.

### Repository context helps Copilot trace code and suggest changes with security advice
[19:28](https://www.youtube.com/watch?v=uhoPM-ABuV0&t=1168s)
In the Parts Unlimited repository, Bernes asks where sales tax is calculated. Copilot finds the default shipping tax calculator, identifies the calculate tax function, and lists other callers. It also sees that the code hardcodes a Washington tax rate. After a web search for public sales-tax APIs, Bernes asks how to update the function to use the Avalara API. Copilot proposes a code change and warns that the API key must not be hardcoded. It recommends storing the secret in HashiCorp Vault, Azure Key Vault, or GitHub Secrets.

### Copilot can reduce the work needed to document and review pull requests
[25:11](https://www.youtube.com/watch?v=uhoPM-ABuV0&t=1511s)
Bernes shows Copilot generating a pull request summary from the changes in a request. The result includes a summary paragraph and a file-by-file list of modifications, which gives reviewers more context without requiring the author to write it all manually. Copilot can also explain an individual change. Bernes says reviewers can compare that explanation with the related user story and assess whether the implementation matches its intended behavior.

### The same assistance applies to older languages and modernization work
[27:33](https://www.youtube.com/watch?v=uhoPM-ABuV0&t=1653s)
Bernes says Copilot works with any language represented in the code used to build its large language models, including COBOL. He asks Copilot to explain a COBOL repository containing a Fibonacci sequence example. He then suggests using the explanation as a starting point for translating the code into Java, C, or another language. The example frames Copilot as a possible aid when teams need to understand or modernize older code.

## Notable quotes
- "It's called GitHub Copilot, it's not called GitHub autopilot." (05:07)
- "Part of prompt crafting is that specificity in your comments, in your requests and things." (05:57)
- "You can ask for help about coding right where you are." (10:15)
- "Make sure you do not hardcode that API key in your code." (23:49)
- "Use this as a way to modernize your COBOL and other code." (28:26)

## Tools & references mentioned
- GitHub Copilot Individual
- GitHub Copilot Business
- GitHub Copilot Chat
- GitHub Copilot Enterprise
- GitHub Copilot extensions
- GitHub Codespaces
- Visual Studio Code
- Visual Studio
- JetBrains
- PyCharm
- IntelliJ IDEA
- Django
- Octopus Deploy
- MongoDB
- DataStax
- Azure DevOps
- Bitbucket
- Team Foundation Version Control
- GitHub Actions
- Avalara
- HashiCorp Vault
- Azure Key Vault
- GitHub Secrets
- COBOL
- The Phoenix Project
- Jean Kim
- Copilot Workspace

## Who should watch
- You use Copilot for inline suggestions and want practical examples of Chat, Enterprise, and pull request features.
- Your team needs repository-aware answers, internal documentation search, or deployment information without switching tools.
- You maintain older code or inherited code and want help explaining, testing, debugging, or translating it while keeping review responsibility with developers.

## Related talks

- [GitHub Copilot: The World's Most Widely Adopted AI Developer Tool](https://aietalks.com/talks/github-copilot-the-worlds-most-widely-adopted-ai-developer-tool-skituuev) (Christina Warren, GitHub & Dave, GitHub and Azure DevOps Teams & Alex, GitHub for Startups & Harold, Microsoft, 1:19:45)
- [The Agent Awakens: Collaborative Development with Copilot](https://aietalks.com/talks/the-agent-awakens-collaborative-development-with-copilot) (Christopher Harrison & John Peck, GitHub, 1:04:06)
- [Copilots Everywhere](https://aietalks.com/talks/copilots-everywhere) (Thomas Dohmke, GitHub & Eugene Yan, 18:22)
- [Piloting agents in GitHub Copilot](https://aietalks.com/talks/piloting-agents-in-github-copilot) (Christopher Harrison, Microsoft, 59:07)
- [The AI Evolution](https://aietalks.com/talks/the-ai-evolution) (Mario Rodriguez, GitHub, 19:32)