GitHub Copilot Beyond the Basics - 10 Ways to Elevate Your Coding

Can AI do more than just write code? Learn 10 ways GitHub Copilot can refactor, debug, and even generate entire unit tests for you.

#1about 2 minutes

What GitHub Copilot is and how it helps developers

GitHub Copilot is an AI-powered code completion tool that helps developers stay in the flow by handling repetitive tasks and providing instant suggestions within the IDE.

#2about 1 minute

Getting started with Copilot licenses and IDEs

To begin using Copilot, you need a GitHub account, a license (Individual, Business, or Enterprise), and a supported IDE like VS Code or JetBrains.

#3about 2 minutes

How Copilot processes your code behind the scenes

Copilot collects context from your code, sends it through a proxy server for filtering, gets suggestions from an LLM, and then returns the filtered results to your editor.

#4about 1 minute

Understanding the GPT models that power Copilot

Copilot utilizes different versions of OpenAI's Generative Pre-trained Transformer (GPT) models, using GPT-3.5 for faster completions and GPT-4 for more powerful chat capabilities.

#5about 4 minutes

Using core features like auto-suggestions and commands

Leverage Copilot's core functionality by managing open tabs for context, writing comments in any language to generate code, and using slash commands for common tasks.

#6about 4 minutes

Accelerating development by generating various types of tests

Streamline the testing process by using Copilot to generate unit tests for isolated components, follow a TDD workflow, or create acceptance tests with frameworks like SpecFlow.

#7about 1 minute

Troubleshooting errors and improving code performance

Use Copilot to debug exceptions by explaining errors and suggesting fixes, or ask it to identify and implement more efficient algorithms to optimize performance.

#8about 2 minutes

Generating UI components and UI tests from models

Backend developers can simplify frontend tasks by asking Copilot to generate UI forms based on data models and then create corresponding UI tests.

#9about 3 minutes

Applying prompt engineering for more accurate results

Improve the quality of Copilot's output by using prompt engineering techniques like zero-shot, few-shot, and Chain-of-Thought to provide better context and examples.

#10about 7 minutes

Live demo of practical Copilot use cases

A live demonstration shows how to use Copilot to generate methods from comments, create unit tests, debug runtime errors, and significantly optimize a slow function.

#11about 2 minutes

Best practices for working with AI assistants

To work effectively with Copilot, set clear goals, provide iterative feedback, and always review the generated code instead of blindly trusting its output.