Complete spec-driven development assessment: Task Management API

[srg-cmyk]

PR Submission Summary by Sijo Rejigeorge.

Approach:
I used a spec-driven, AI-assisted development workflow by providing comprehensive project requirements for the application and the features that needed to be implemented. I prompted Claude Sonnet 4.5 to first generate detailed feature specifications based on those requirements and then iteratively implement the solution following the generated specifications. I provided structured prompts and clear constraints, explicitly instructing the model to follow the project’s rules.md guidelines, templates, and architectural requirements throughout the process.
All generated specifications and code were carefully reviewed, validated, and manually refined to ensure correctness, clarity, and maintainability. The final solution follows clean architecture principles, with a clear separation of concerns (Models → Repository → Service → Routes), and includes comprehensive test coverage.

Application Overview
A fully functional backend-only RESTful API for task management with the following features.

• Create tasks with title, description, status, and completion flag
• Read all tasks or individual tasks by ID
• Update tasks (full or partial updates)
• Delete tasks
• Search and Filter tasks

Tools Used:
Claude AI: Primary code generation tool used for development acceleration
FastAPI: Modern Python web framework with automatic API documentation
Pytest: Testing framework with coverage reporting
Pydantic: Data validation and schema modeling
Uvicorn: ASGI server

What Helped:
Writing detailed specs before coding provided clear targets for AI generation
Iterative testing during development caught issues early
Using repository pattern enabled easy testing and future database migration
Claude generated high-quality, idiomatic Python code with proper validation from the start
Starting with test requirements ensured comprehensive coverage wasn't an afterthought