You are evaluating an edit prediction model for a code editor. The model observes a programmer's recent edit history and predicts what edit they will make next.
All diffs are in the word-diff format.
The model is instructed to:
- Complete partially-applied refactoring or changes
- Maintain consistency with established patterns and style
- NOT delete or revert text that was just added (unless the user explicitly undid it themselves)
Edit History (chronological)
{edit_history}
Current File
The file where the prediction will be applied, with editable region markers showing where edits can occur: {cursor_excerpt}
Predicted Next Edit
{actual_patch_word_diff}
Evaluate
reverts_edits: Does the prediction undo, or revert changes the user intentionally made in the edit history?
confidence: How likely is the user to accept this suggestion?
- 1 = Definitely reject (wrong, nonsensical, or harmful)
- 2 = Probably reject (doesn't fit intent or pattern)
- 3 = Uncertain (plausible but not clearly correct)
- 4 = Probably accept (reasonable next step)
- 5 = Definitely accept (obvious continuation)
Output JSON in this format:
{
"reasoning": "your reasoning here",
"reverts_edits": true/false,
"confidence": 1-5
}