Most teams use GitHub Copilot like autocomplete. The best teams structure their projects so Copilot works like a real engineering assistant. GitHub Copilot gets much better when your repo gives it clear context. Not just code. But instructions, conventions, workflows, and boundaries. A practical GitHub Copilot project structure could look like this: /.github/copilot-instructions.md Core guidance for how Copilot should behave across the repository. /.github/instructions/ Modular instruction files for specific domains: 👉 frontend.instructions.md 👉 backend.instructions.md 👉 testing.instructions.md 👉 api.instructions.md /docs/architecture/ System design, domain boundaries, key technical decisions. /docs/conventions/ Naming rules, folder patterns, coding style, review expectations. /prompts/ Reusable prompts for recurring tasks: 🎯 refactoring 🎯 writing tests 🎯 generating API handlers 🎯 reviewing PRs /examples/ Golden examples Copilot can mimic: 🎯 preferred component patterns 🎯 API route templates 🎯 test structure 🎯 error handling patterns /scripts/ Helper scripts for validation, formatting, codegen, and local workflows. The point is simple: AI coding tools perform better in well-structured environments. If your repository is messy, undocumented, and inconsistent, Copilot will reflect that. If your repository is structured, explicit, and opinionated, Copilot becomes far more useful for: 👉 generating code that matches your standards 👉 following existing architecture 👉 producing better tests 👉 reducing review friction 👉 speeding up onboarding Good AI output starts with good project design. We should stop asking only: "Which AI coding tool is best?" And start asking: "Is our codebase designed to make AI effective?" That is where the real leverage is. #GitHubCopilot #AIEngineering #DeveloperTools #SoftwareArchitecture #EngineeringProductivity #CodingWithAI #DevTools #ContextEngineering
Good point. Prompts are useful, but agent skills fill like the more powerful layer.
zanimljivo 👏