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AI coding tools are powerful. With the right prompt, you can spin up a prototype in minutes. But if you’ve tried, you’ve probably seen both sides:
✨ Magic — features appear like you have a 10x engineering team.
😵 Chaos — hallucinated code, broken dependencies, wasted hours fixing mistakes.
The difference isn’t the tool. It’s the workflow you follow.
Here’s a proven 3-step system—adapted from real-world builders—that helps you ship reliably with AI coding assistants like Cursor, Claude Code, Windsurf, or even CLI agents.
Step 1 — Write a Mini PRD (yes, even for small features)
Most people jump straight to “generate code.” That’s where mistakes happen.
Instead, start with a lightweight Product Requirements Document (PRD). It doesn’t have to be corporate or long—just a one-pager that forces clarity.
Why it helps
Gives the AI clear context.
Forces you to articulate what problem the feature solves.
Reduces back-and-forth because the scope is explicit.
What to include
Problem & Goal (why you’re building it)
Users (who it’s for)
Success Metrics (what “done” looks like)
Functional Requirements (numbered list)
Non-Goals (what’s out of scope)
Clarifying Questions (dot notation: 2.1, 2.2, …)
Quick method
Keep a
create-prd.mdfile in your project (template prompt).In your AI tool, reference it:
Use @create-prd.md
Here’s the feature I want to build: [describe in detail]
Reference these files if relevant: [@file1.ts @file2.py]
👉 Pro tip: In Cursor, use MAX mode for richer PRDs if you can.
Prompt template:
Write a Product Requirements Document suitable for a junior developer.
Include: Problem, Goal, Users, Success Metrics, Functional Requirements, Non-Goals, Design Considerations, and Clarifying Questions in dot notation.
Feature request: “Add a report that lists each member’s boat name and the number of emails they’ve received.”
Ask clarifying questions before drafting. Keep it implementation-ready.
👉 Even for a small prototype (like “add a search bar”), a PRD makes AI outputs far more accurate.
Step 2 — Break it into a Task List
Once you have a PRD, translate it into a Markdown task list.
Why Markdown? Because it’s simple, visible, and easy to check off.
Why it helps
Keeps AI from trying to do too much at once.
Turns a big vague “build this feature” into smaller, testable steps.
Helps you track progress like a mini project board.
How to structure it
Start with “Relevant Files” → only the files AI should touch.
Use hierarchy:
1, 1.1, 1.1.1.Add checkboxes
[ ]→ mark[x]when complete.Tell the AI to stop after each subtask and wait for your confirmation before continuing.
Quick method
Use a
generate-tasks.mdfile with your PRD. Prompt:
Now take @MyFeature-PRD.md and create tasks using @generate-tasks.md
The result: a
tasks-MyFeature.mdfile with checkboxes, numbered hierarchy (1, 1.1, 1.1.1).Add a “Relevant Files” section at the top for clarity.
Prompt template:
Generate a step-by-step task list in Markdown from the PRD.
- Begin with "Relevant Files".
- Group work as 1, 1.1, 1.1.1, etc.
- After generating the list, stop and wait for "go".
- Execute exactly ONE subtask at a time.
- Mark completed subtasks with [x] and add a one-line “What changed”. 
Generated Task List
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Step 3 — Execute One Subtask at a Time
This is where most people lose control. They ask AI to “just build it” — and the tool changes 12 files, introduces bugs, and drifts off-spec.
Instead:
Run one subtask at a time.
After completion, review + test quickly.
Only then → say “go” for the next task.
Commit changes at safe checkpoints (end of each parent task).
AI will attempt the task and then prompt you to review
Tips for control
Always tag the exact files in context.
If the model drifts, reset it:
“Re-read the PRD and Task 1.2 only. Explain in 3 bullets, then execute.”Stay polite but firm — treat it like a junior developer you’re guiding.
Quick method
Use a
process-task-list.mdfile with clear rules:Work on one subtask at a time.
Mark it
[x]when complete with a short note.Stop and wait for “go” before continuing.
Example:
Please start on task 1.1 and use @process-task-list.md
Then review → approve → proceed.
Optional Power-Ups
Context Tools → Some apps let you select specific files/folders to give AI exact context (e.g., Repo Prompt).
Integrations (MCPs, Plugins, etc.) → Connect to databases (Postgres), browsers (for testing), or APIs.
CLI Task Runners → Tools like Taskmaster automate task-list-driven coding loops if you want more structure.
Music Stack 🎶 → Coding in flow matters. Pair this workflow with your best focus playlist.
🛠️ Tool-Specific Tips
Cursor → Reference
.mdfiles with@. Use MAX mode for complex PRDs.Claude Code → Place files in
/ai-dev-tasksand create slash commands (/create-prd,/generate-tasks,/process-task-list).Other IDEs/CLIs → Just copy the
.mdfiles into your project and adapt prompts.
Why This Workflow Works
This 3-step loop replaces the missing structure that teams normally provide:
PRD → acts like your product manager.
Task List → acts like your engineering manager.
Subtask Execution → keeps you in control as the senior dev.
It’s not about replacing jobs. It’s about giving anyone (developer, founder, student, side-hustler) a way to harness AI coding tools effectively.
Key Takeaways
Don’t skip context → 10 minutes writing a PRD saves hours fixing bugs.
Turn big asks into small, checkable steps → task lists are your friend.
Execute one subtask at a time → test, commit, move forward.
Use power-ups (context control, integrations) to scale the workflow.
Why this matters now
In 2025, the barrier to building apps isn’t technical skill—it’s workflow discipline. AI coding tools are powerful, but without structure, they produce chaos.
With this simple 3-step system, you can:
Prototype an idea in days instead of weeks.
Build MVPs without hiring large teams.
Stay focused and avoid AI coding “drift.”