Fine-Tuning

Fine-tuned models learn from your specifications, codebase patterns, and corrections. They produce output that matches your team's style better than general-purpose models.

→ Fine-Tuning Studio

When to Fine-Tune

Fine-tuning helps when:

  • You have consistent team conventions the general model doesn't follow
  • You make frequent manual corrections to generated code
  • You have a large codebase with unique patterns
  • Your domain uses specialized terminology

Skip fine-tuning if you're just starting with Aexol — build a Knowledge Base first.

Creating a Model

  1. Go to /studio/fine-tuning
  2. Create a new fine-tuning job
  3. Select training data: .aexol specs, generated artifacts, manual corrections
  4. Configure training parameters
  5. Start training

Training Data Quality

Good: diverse examples, consistent style, corrected output showing "right" answers

Bad: duplicates, inconsistent style, uncorrected errors

Using Fine-Tuned Models

Once trained, the model appears in:

  • Studio — generate and import panels
  • MCP — reference by name in remote_start_inference
  • Agent — select as preferred model

Model Management

  • Versioning — train new versions as your codebase evolves
  • Team sharing — models shared across team members
  • Compare — side-by-side output at /studio/fine-tuning

Next Steps