Step-by-step Master Guide for Building Features with AI Coding Agents
Here is your step-by-step master guide for building features with AI coding agents before you start a coding project or feature using AI assistance (like GitHub Copilot, Claude Code, or AWS Q Developer). Phase 1: Structuring the Repository for Maximum AI Context Before writing a single line of code, the AI agent needs a clean environment to "understand" your system architecture. 1. Establish an AI Rules File ( .cursorrules or CLAUDE.md ) Place a context rule file in the root of the repository. Define architectural constraints, coding standards, language versions, and testing requirements. Example: Specify "Python 3.12, strict type hints, Pydantic v2 for schema validation, Pytest for unit tests, no raw SQL (use SQLAlchemy ORM)." 2. Implement a Modular, Clean-Architecture Folder Structure Keep directory structures predictable so the AI agent doesn't hallucinate paths or mix concerns: /src/domain/ (Data models, schemas, entities) /src/services/ (Business logic,...