Guide to Choosing the Right AI Model for GitHub Copilot

Introduction

• GitHub Copilot offers multiple AI models for chat and code completion.
• Developers often use different models for chat and code completion.
• Reasoning models are preferred for ensuring technical correctness.
• New models should be evaluated based on responsiveness, correctness, and quality signals.

Chat vs. Code Completion

• Different models are used for chat and code completion.
• Latency is more tolerable in chat, allowing for more exploration.
• Reasoning models are preferred for ensuring technical correctness in code completion.

What to Look for in a New AI Model

• Up-to-date model
• Responsiveness in generating suggestions
• Correctness in the generated code
• Quality signals beyond basic functionality

How to Test an AI Model in Your Workflow

• Check the structure and correctness of the generated code.
• Evaluate the performance of the model in code autocompletion.

Use it as a "Daily Driver" for a While

• Start using the new model in your workflow.
• Evaluate the model over a period of time before making a decision.

Take this with you

• Use different models based on the task at hand.
• Regularly evaluate and experiment with new AI models to stay updated.

Additional Resources

• Choosing the right AI model for your task
• Examples for AI model comparison
• Which AI models should I use with GitHub Copilot?

Tags: AI models, generative AI, GitHub Copilot, reasoning models Written by Klint Finley @klintron