Write Linters and Tools Before Code (cookie.engineer)

The article advocates for developing linters and tools prior to deploying non-deterministic language models for document generation. It highlights that while language models excel at producing text, they struggle to maintain consistent structures across longer documents due to their lack of persistent memory. The key insight is that the generated content's quality can be greatly improved by pre-encoding structural rules in executable formats like linters and parsers, which provide deterministic checks and constraints, thus mitigating the risk of structural inconsistencies that often plague output from language models. This approach underscores the importance of creating a framework where decisions about document structure are made by the tools rather than left to the model during generation. By specifying constraints such as allowed elements, nesting depths, and necessary metadata fields, linters can enforce strict adherence to document standards. As a result, much of the responsibility for document quality shifts to these tools, allowing the model to focus on low-level tasks, which ultimately leads to more consistent and valid outputs. This method not only enhances document quality but also establishes a clear architecture that supports scalability in AI-driven content generation.