Dignified Python: 10 Rules to Improve your LLM Agents

Modern large language models (LLMs) generate code based on patterns from a diverse mix of sources, often resulting in inconsistent outputs that lack coherent design principles. To address this, Dagster has developed a set of guidelines called "Dignified Python" to ensure that code produced by LLMs reflects their engineering standards and philosophy. These rules are integrated into the model's context to maintain clarity and consistency, emphasizing proactive checks over reactive exception handling, avoiding silent error swallowing, ensuring performance efficiency, and using explicit coding practices. The guidelines aim to produce code that not only functions correctly but also communicates intent clearly, thereby enhancing engineering velocity without compromising quality. By encoding their expertise into these rules, Dagster ensures that AI-generated code aligns with their architectural decisions and standards, ultimately fostering a more unified and deliberate codebase.