# Model Refinement Process
> Source: https://docs.trailspark.ai/docs/model-refinement
## How Refinement Works
Model refinement updates your scoring model based on accumulated feedback. The process:
1. **Feedback accumulates** -- team members submit corrections on lead evaluations
2. **User triggers refinement** from the Training Models page (requires Owner, Admin, or Editor role)
3. **Preview model is generated** -- AI analyzes all pending feedback alongside the current model
4. **Test evaluation runs** -- the preview model scores feedback leads so you can compare old vs. new scores
5. **User saves and applies** -- new model version is created and becomes active
6. **Leads are re-evaluated** -- leads from the last 30 days are queued for re-scoring with the updated model
> [!NOTE]
> Refinement is manually triggered, not automatic. Submitting feedback does not start a refinement on its own.
## Triggering a Refinement
### Prerequisites
- **Owner, Admin, or Editor** role
- At least 1 pending feedback submission
- Available refinement capacity on your plan
### The Refinement Flow
On the **Training Models** page, a notification appears when pending feedback is available:
1. Click **Preview Refinement** to open the refinement modal
2. **Review pending feedback** -- see each feedback item with the lead, account, feedback text, and submission date
3. Click **Generate Preview Model** -- AI creates a candidate model incorporating your feedback
4. **Review the preview** -- see the refinement summary and model summary describing what changed
5. Click **Test on Feedback Leads** -- run the preview model against feedback leads and compare old scores (Old Status) vs. new scores (New Status)
6. Click **Save & Apply Model** -- creates a new model version and triggers re-evaluation of leads from the last 30 days
## What Happens During Re-evaluation
After committing a refinement:
- A new model version is created and set as active
- The previous model version is set to Inactive
- Leads evaluated within the last 30 days are queued for re-scoring
- Score changes (Cold to Warm, Hot to Warm, etc.) appear as leads are re-evaluated
- Score history is preserved
Re-evaluation is queued, not instant. Results typically appear within hours depending on the number of leads.
## Refinement Limits
Refinements are metered by your plan. The Training Models page displays:
- Refinements used vs. limit for the current billing cycle
- Remaining refinement credits
When the limit is reached:
- The **Preview Refinement** button is disabled
- A message indicates all refinement credits have been used and suggests upgrading the plan
- Feedback submission is also blocked on the lead detail page until capacity is restored
## Viewing Model History
The Training Models page lists all model versions with:
| Column | Description |
|--------|-------------|
| **Status** | Active or Inactive |
| **Name** | Model name (or display name / version fallback) |
| **Version** | Model version ID |
| **Created On** | When the model was created |
| **Actions** | Set as Active (for inactive models), View Details |
## Getting the Most from Refinement
**Batch feedback before triggering.** A single refinement with 10 feedback items produces better results than 10 individual refinements with 1 item each. Wait until you have a meaningful set of corrections.
**Focus on clear misscores.** Feedback on borderline cases is less useful than feedback on obviously wrong scores. Prioritize leads where the score is clearly misaligned with your criteria.
**Review test results before committing.** The test evaluation step exists specifically so you can verify the refinement moves scores in the right direction before it goes live.
## Next Steps
- [Evaluation Feedback](/docs/evaluation-feedback) -- Submit corrections on lead scores
- [Usage Tracking](/docs/usage-tracking) -- Monitor refinement capacity
- [ICP Overview](/docs/icp-overview) -- Revisit your ICP definition