You know the one the one where your changes are clear, concise, and easy for others to understand.

But wait, what’s this “Customizing LLMs” thing you ask? Let me explain. You see, GitHub Copilot Enterprise uses Large Language Models (LLMs) to help you write code faster and more accurately. But sometimes those models can be a bit…well, let’s just say they don’t always understand your specific use case or project requirements.

That’s where customizing comes in! By fine-tuning the LLM on your own data, you can train it to better understand your codebase and generate more relevant suggestions for pull requests. And the best part? It’s super easy to do with GitHub Copilot Enterprise!

Here are some simple steps to get started:
1. First, head over to your project settings in GitHub and click on “Copilot” under the “Code & automation” section.
2. Click on “Customize model” and select the language you want to fine-tune (e.g., Python).
3. Choose a dataset or upload your own data for training. You can use GitHub’s pre-trained datasets, or create your own using tools like Pandas or CSVKit.
4. Set up your training environment and run the script provided by GitHub Copilot Enterprise to fine-tune the LLM on your chosen dataset. This will take some time depending on the size of your data and the complexity of your model, but it’s worth it for those sweet, sweet pull request suggestions!
5. Once training is complete, test out your customized LLM by creating a new pull request with GitHub Copilot Enterprise. You should notice that the suggestions are more relevant to your specific use case and project requirements.

And there you have it faster, smarter pull requests thanks to GitHub Copilot Enterprise! So go ahead, give it a try and let us know how it works for you in the comments below. And if you’re feeling extra fancy, why not share your customized LLM with others using GitHub Copilot Enterprise’s sharing feature?

Later!