A five-step process for building production-ready AI apps

Executive overview

AI-assisted app development breaks down when there's no structure: the model chases errors, loses sight of the goal, and breaks features without a way to recover. A five-step process — design, brain dump, implementation plan, reasoning model review, markdown conversion — keeps the AI grounded from macro goal to micro task.

The core insight: treat the implementation plan as a living markdown checklist the AI references throughout the build, not a one-time prompt.

The five-step process

1. Design phase — dictate the app idea, target persona, and job to be done to an AI designer; iterate on UI mockups until satisfied; screenshot all key views
2. Brain dump — feed screenshots and dictated context into an implementation-plan-builder Claude project; specify this is for V1 (no CI/CD, minimal security, deployable but not production-hardened)
3. Implementation plan output — the system prompt instructs the AI to produce phases with subsections and micro-steps, each with a checkbox; structured for an inexperienced engineer needing hyper-detailed guidance
4. Reasoning model review — paste the plan into O1 or DeepSeek R1; ask what's missing for a new engineer; keep only the 1–2 suggestions that apply to V1; ask the model to rewrite the full plan with additions, then iterate 2–3 times to ensure nothing is lost
5. Markdown conversion — convert the final plan to markdown using Claude 3.5 Sonnet or GPT-4o; paste into Cursor's notepad feature so the AI stays anchored to the macro plan while building micro tasks

Why dictation works throughout

• Voice-to-text introduces typos and jumbled words — it doesn't matter; AI infers intent reliably
• Dictation is used at every stage: idea, persona description, jobs to be done, caveats, and V1 scope
• Speed and volume of context outweigh transcription accuracy

Test-driven development in the plan

• Each phase requires tests written before any functionality is built
• The AI writes the test, then writes the function, then runs the function against the test
• Failure produces detailed logs the AI uses to iterate — a micro reinforcement learning loop
• End-to-end testing per phase: backend, API, and frontend must connect before moving on

The implementation plan structure

• Top level: phases (each represents a complete, testable end-to-end feature)
• Mid level: subsections within each phase
• Bottom level: micro-steps with checkboxes inside each subsection
• As the AI builds, it checks off completed steps — maintaining awareness of progress without losing the macro goal

Cursor notepad vs. standalone markdown file

• The same markdown plan kept in Cursor's notepad feature produces better results than a separate file
• The AI focuses more reliably on the plan when it lives in notepad
• Check boxes are marked off incrementally as phases complete