How should we evolve dev practices for the AI programming era?

Greetings SWEs of Reddit! I've recently started working in the specialized LLM space for software development, and something has been nagging at me: our development practices haven't really evolved to account for AI programming assistants. While we've eagerly adopted tools like Copilot and Claude, we're still documenting and structuring our code like it's 2019.

This feels particularly crucial in enterprise environments. Think about how senior developers approach complex systems—they draw upon years of accumulated knowledge about security boundaries, performance requirements, and service dependencies. This context, often held as tribal knowledge or scattered across wikis and decision records, is precisely what AI assistants struggle to understand.

Some key thoughts on what evolution might look like:

1. Moving beyond human-readable documentation to machine-readable context about system constraints and design decisions
2. Structuring code and metadata in ways that help AI understand architectural boundaries and operational requirements
3. Capturing implicit knowledge about security and compliance constraints in formats AI can reliably process
4. Creating automated ways to maintain this context without burdening developers

I'm particularly curious to hear from other enterprise developers:

• How do you currently handle documenting system constraints and architectural decisions?
• What challenges do you face when using AI assistants with complex enterprise codebases?
• What would make it easier for you to provide better context to AI tools without slowing down development?

I wrote a more detailed analysis of this challenge [here] if anyone's interested, but I'm really looking forward to hearing your thoughts and experiences. How do you think our development practices need to evolve for effective AI collaboration?