As I'm always careful to shape the context right with my initial prompts and when I go back to AI chats, which seemed to work best for me, I find this approach quite valuable and I am curios to see more examples about how you do it.
3 weeks ago (edited) | 0
That is great if you're working "solo" and the team is onboard with AI But, in a team of developers where they are still arguing what library is the best😅 Is a waste of tim
3 weeks ago | 0
Codewrinkles
I’ve been building systems AI-assisted for a while now — and here’s the biggest lesson: 👇
❗The quality of what you get from AI depends 90% on the context you give it.
💡 So instead of writing better prompts, I started writing better context.
In every repo I have multiple CLAUDE.md files:
🧩 At the root → high-level architecture, dependencies, testing rules, CI/CD expectations.
🧩 Per subfolder (like iac/, backend/, frontend/) → language-specific guidelines, design patterns, and “what good looks like” examples.
🧩 Per domain → business context, boundaries, and key entities.
This way, when I ask the model to add a feature or fix something, it already understands the system — not just the syntax.
The result?
✅ Less prompting.
✅ More reasoning.
✅ Consistent code that feels like it came from the same engineer.
The real productivity boost isn’t from writing faster. It’s from teaching your AI how your system thinks.
#SoftwareEngineering #AI #Architecture #CleanCode #DevTools
3 weeks ago (edited) | [YT] | 12