Comment by jlcases

I've found that the quality of LLM output depends enormously on how you structure the context. After experimenting with various documentation approaches, I've had the best results using MECE principles (Mutually Exclusive, Collectively Exhaustive) to organize project knowledge.

For Python/data engineering specifically, creating clear category boundaries between data models, transformation logic, and validation rules makes the LLM much more likely to generate code that follows your architectural patterns.

The key is treating documentation as a "context API" for the AI rather than just human-readable text. When your documentation has a clear hierarchical structure without overlaps or gaps, the LLM can navigate the problem space much more effectively.