AI Dev Tasks: A Framework for Manageable AI-Assisted Development

July 24, 2025

I discovered a game-changing approach to working with AI coding assistants that’s transformed how I handle complex development tasks.

The Problem

When I first started using AI for coding, I’d dump an entire feature request and hope for the best. The results were unpredictable - sometimes brilliant, often a mess. The AI would confidently generate hundreds of lines of code that looked right but had subtle bugs or architectural issues. By the time I realized something was wrong, I’d already committed to an approach that needed major refactoring.

The Solution: Three Strategic Markdown Files

I found the AI Dev Tasks framework which breaks AI-assisted development into three manageable phases:

1. Product Requirements Document (PRD)

The create-prd.md prompt guides the AI to generate a proper PRD for your feature. Instead of jumping straight into code, you first establish:

Clear scope and requirements
Success criteria
Technical constraints
User stories

2. Task Decomposition

The generate-tasks.md prompt takes your PRD and breaks it into granular, sequential tasks. This is where the magic happens - complex features become a series of small, verifiable steps. Each task is specific enough to implement and test independently.

3. Iterative Implementation

The process-task-list.md prompt instructs the AI to tackle one task at a time, waiting for your approval before proceeding. This creates natural checkpoints where you can:

Verify the implementation matches expectations
Catch issues early
Adjust course if needed
Maintain control over the development direction

Why This Works

The framework transforms AI coding from a black box into a transparent, controlled process. Instead of hoping the AI understands your entire vision, you’re guiding it through manageable chunks with verification at each step.

I’ve used this approach on several projects now, and the difference is dramatic. What used to be a risky all-or-nothing proposition is now a predictable, iterative process. The AI still does the heavy lifting, but I maintain meaningful control over the architecture and implementation details.

Key Learnings

Decomposition is crucial - Breaking work into small tasks prevents runaway AI generation
Checkpoints create safety - Regular approval points catch issues before they compound
PRDs improve AI understanding - Clear requirements lead to better implementations
The framework is tool-agnostic - Works with Claude, GPT-4, or any capable AI assistant

This framework has become an essential part of my development workflow. If you’re struggling with unpredictable AI coding results, give it a try - it might transform your experience too.