Developed a context-aware string literal completion feature for the astral-sh/ruff repository, enhancing the Python IDE experience by providing string suggestions based on expected type inference. This work integrated type inference logic into the completion engine, allowing the IDE to offer more relevant string literals during code writing and reducing type-related errors. The implementation leveraged both Python and Rust, focusing on IDE development and performance optimization. Collaboration with project maintainers ensured the feature aligned with existing conventions and met performance goals. No major bugs were addressed during this period, with efforts concentrated on feature delivery, code quality, and ongoing maintenance.