ratel

Ratel is an AI/ML platform for context engineering that enables developers and data scientists to build, manage, and optimize the contextual data pipelines powering large language model applications. It provides tools to structure, version, and test context inputs—such as prompts, retrieval-augmented generation (RAG) sources, and system instructions—to improve model accuracy and reliability. The project is interesting because it addresses the critical but often overlooked challenge of systematically engineering context, moving beyond prompt engineering to treat context as a first-class, iterable component of AI systems.