Context-Aware Completion

Basic code completion suggests what comes next based on the current file alone. Context-aware completion extends this by considering the broader codebase: the function signatures of imported modules, the types defined in related files, the naming conventions established in sibling files, and the project's dependency tree.

Achieving context-aware completion requires codebase indexing — processing the entire project to build embeddings or symbol tables that can be efficiently retrieved and included in AI prompts. Tools like Cursor index your repository on first open and keep the index updated as you edit. The retrieval step selects the most relevant context to include within the model's token limit.

The quality difference between basic and context-aware completion is most apparent in large codebases with significant internal abstractions. When the model knows that your project uses a specific ORM with custom query patterns, or a design system with named tokens, completions that reference these correctly feel qualitatively different from generic suggestions.