Developed a dataset compatibility enhancement for the axolotl-ai-cloud/axolotl repository, focusing on robust support for messages fields stored as JSON strings. The solution introduced handling for both string and array formats of messages and tools, addressing common data ingestion issues and improving downstream model training reliability. Leveraging Python for implementation and unit testing, the work emphasized maintainable code updates and comprehensive test coverage to prevent regressions. By expanding compatibility with diverse dataset formats and external data pipelines, this contribution laid the groundwork for smoother model training workflows and improved data processing, demonstrating strong skills in JSON handling and test-driven development.