What this prompt does
This prompt produces a guide for making Cursor's tab completions more accurate in your specific project. You set the [language]/[framework], describe the [current_quality] of suggestions you see now, and the AI explains how to structure code, comments, and types so completions infer the right thing. It covers leading comments with [example_count] examples for [common_patterns], the optimal file structure for context, a typing strategy in [types_location], docblock annotations Cursor leverages, test-file patterns, an accept-modify-reject habit, and the settings.json values that affect completion quality.
The structure works because tab completion is fundamentally context-driven: it predicts from what is already on the page and in nearby files. Leading comments prime the model toward the implementation you actually want, so writing the comment before the body changes the suggestion. Explicit types centralized in [types_location] let Cursor infer return types and parameters instead of guessing shapes, which is where wrong-import and wrong-shape suggestions come from. Well-named describe and it() blocks let it generate real test cases from the names alone. The accept-modify-reject discipline keeps your context clean over time. The prompt turns vague frustration with [current_quality] into concrete, mechanical changes you make to your code and your editor settings, one at a time.
When to use it
- Tab completions feel hit-or-miss and you want a systematic fix rather than guessing at the cause
- Cursor keeps suggesting wrong imports or outdated patterns for your
[framework] - You are setting up a new project and want completions to be accurate from the start
- Your types are scattered across the project and completions cannot infer parameters or return values
- You want to write test files where describe blocks alone drive useful, relevant suggestions
- You suspect your
settings.jsonis leaving completion quality on the table
Example output
You get a practical guide: example leading comments for your [common_patterns], a recommended file structure for where types and utilities live, a typing strategy for [types_location], docblock patterns Cursor leverages, test-file patterns, guidance on when to accept or reject suggestions, and a list of settings.json keys with recommended values. It reads as a checklist you apply directly to your real project rather than abstract advice.
Pro tips
- Describe
[current_quality]honestly with examples of the wrong suggestions you see; it sharpens the diagnosis - Centralize types in
[types_location]with clear exports — completion accuracy jumps when return types are explicit - Write the leading comment before the function body; the model completes toward the intent you just stated
- Name describe and
it()blocks descriptively so test completions have something concrete to work from - Apply the
settings.jsonchanges one at a time so you can tell which one actually helped - Build a habit of rejecting wrong suggestions outright rather than fixing them after, which keeps your context clean
