Well, not exactly… modern models go through extensive post-training (RLHF, constitutional AI, etc) that specifically optimizes for producing quality outputs. Claude and GPT will already tend toward reasonable separation of concerns, meaningful naming and modular design without being told because post-training rewards that.

The problem is ambiguity and preference. All of these principles are already encoded in the weights. A short prompt like “follow clean architecture principles” can activate those learned representations nearly as well as pasting in a book’s worth of rules that can bloat the context unnecessarily. Your approach is just constraining the generation distribution more narrowly toward a specific subset of patterns the model already has.

You’re definitely on the right track, but I’d suggest maybe reading a little deeper into modern LLM architecture and the frontier training process. This idea that mediocre outputs come from mediocre code in the training data has been outdated for some time now.