How to Fine-Tune Refact.ai on Your Own Codebase

Generic assistants suggest idioms your team stopped using three years ago. Refact.ai can train on your repositories so completions match your actual conventions, internal libraries included.

Direct answer: Refact.ai supports fine-tuning on your own repositories by building a training set from your code and running a local training job; the payoff is completions that reference internal APIs, but it takes real GPU hours and only helps on code the model hasn’t seen.

When is fine-tuning worth it?

When your codebase leans on internal frameworks, domain-specific naming, or a monorepo structure that general models fumble. A fintech repo with a custom rules engine, for example, benefits more than a standard React app that popular models already handle well. If your stack looks like everyone else’s, you’ll see less lift.

What data does it need?

Your repositories, ideally clean ones. Point it at the repos you want it to learn and let it extract training examples. Two practical filters:

• Exclude generated files, vendored dependencies, and minified code.
• Skip repos with heavy copy-paste or abandoned experiments — you’ll teach the model your worst habits.

Curating what goes in matters more than volume. Garbage in, confident garbage out.

How do I run training?

1. Connect the source repos to your Refact.ai server.
2. Let it build the dataset from commit history and file structure.
3. Choose a base model that fits your GPU memory.
4. Run the training job and monitor loss.
5. Deploy the tuned model and point your IDE plugin at it.

Expect this to take hours on a single GPU, not minutes. Plan a maintenance window.

How do I know if it actually helped?

Measure before and after on a real task, not vibes. Keep a set of internal files the model hasn’t trained on directly, and compare completion acceptance rates. If your team accepts tuned suggestions more often — and crucially, if they stop correcting internal API names — the training paid off. If not, you’ve spent GPU time for nothing and should revert to the general model.

What are the risks?

Overfitting to old patterns is the big one. Train on a codebase mid-migration and you’ll get fluent suggestions for the framework you’re leaving. There’s also staleness: a model trained last quarter won’t know the API you added last week, so you need a retraining cadence, which keeps adding cost.

Key Takeaways

• Fine-tuning pays off most on internal frameworks and unusual conventions.
• Curate training repos — exclude generated, vendored, and abandoned code.
• Training takes hours on a single GPU, not minutes.
• Measure completion acceptance rates before and after to judge value.
• Models go stale; budget for periodic retraining.