Worked on improving documentation reliability for offline inference workflows in the jeejeelee/vllm repository. Addressed a critical issue by fixing broken links in the Markdown-based documentation, ensuring that users could access accurate code examples for both classification and embedding models. Focused on AI model inference and technical writing, the work emphasized adherence to documentation quality standards and included proper commit signing and attribution. This targeted bug fix reduced user confusion and potential support requests, streamlining onboarding for new users evaluating offline inference. The approach demonstrated attention to detail and a commitment to maintaining accessible, well-structured documentation for the project’s user base.