The problem with AI agents is they lack tribal knowledge
The issue is that code doesn't live in a vacuum. In a real production environment, your codebase is governed by unwritten rules, legacy constraints, and "tribal knowledge" that never makes it into the official documentation. An LLM can write a flawless Python function, but it has no idea that our specific deployment pipeline will choke if we use a certain design pattern, or that we have non-standard naming conventions to satisfy a legacy internal linter.
It’s like hiring a highly skilled contractor who builds a structurally perfect house, but ignores the fact that the local zoning laws or the existing electrical grid won't support the layout. The work is technically "correct," but it's unusable in context.
To keep these agents from polluting our repos with "perfect" code that violates our team's soul, I’ve had to treat prompt engineering like infrastructure management. I’m no longer relying on default settings. Instead, I’ve implemented a .cursorrules file in our repositories. Think of it as a configuration manifest for the LLM—a way to hard-code our architectural decisions and stylistic constraints directly into the agent's context.
It’s an upfront maintenance cost, similar to setting up a CI/CD pipeline, but it’s the only way to ensure the output actually looks like it belongs to our team rather than looking like a generic GPT hallucination. We aren't teaching the AI how to program; we are teaching it our specific engineering culture.
If you want to see how to structure these rule files to minimize friction, these community templates are a good starting point:
https://github.com/features/cursorIf you're using promptcube3.com, you can integrate these rules directly into your workflow to ensure consistency across your entire engineering org.