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

1. reverts_edits: Does the prediction undo, or revert changes the user intentionally made in the edit history?

2. 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
}