A structured three-step process for building apps with AI

Executive overview

AI-assisted development breaks down when builders let the AI take too much control and lose track of what's happening and why. The fix is a structured workflow that keeps the AI anchored to a macro vision while executing in tight micro steps.

Three phases — preparation, design, and control — address the core failure modes: context loss, unstructured prompting, and session drift.

Preparation: the implementation plan

• Create a PRD (Product Requirements Document) as the macro reference the AI always returns to.
• Structure: product overview (3–5 sentences), tech stack, functional and non-functional requirements, future enhancements, best practices.
• For enterprise apps, replace vague phases with prescriptive micro steps — each with checkboxes the AI marks off as it progresses.
• Break the build into phases where each phase produces a small end-to-end working application (backend connected to frontend, APIs functional).
• Start each phase with tests written before the feature is built — failing tests give the AI specific feedback to self-correct, avoiding repeated errors.
• Non-functional requirements (performance, security) get the same micro-step treatment and are tested against the same test suite.

Design: voice-first UI iteration

• Use a Claude project (e.g. "app designer") to generate a design doc from a dictated vision of the app.
• Feed the design doc into the same Claude project to produce a React mockup; iterate on it by dictating changes while viewing screenshots.
• Roughly 80% of UI iteration time is voice — faster than typing, and AI handles the speech-to-text-to-code loop effectively.
• Transfer the screenshot and code to Bolt.New to polish the frontend; Bolt is strong at UI and weak at full-stack, so use it narrowly.
• Bolt.New can deploy a mockup to Netlify — useful for sharing with users before any real backend is built.
• Total design phase: roughly 1–3 hours depending on complexity.

Control: keeping Cursor on track

• Add the implementation plan to Cursor's Notepad feature rather than a plain markdown file — it focuses the model more reliably than cursor rules alone.
• Cursor rules (.cursorrules) are useful but inconsistent; Notepad is the more reliable anchor.
• After every 3–5 Composer Agent exchanges, re-attach the master plan via Notepad to reset context.
• Context drift — the AI gradually forgetting earlier decisions or repeating errors — is the primary failure mode in long sessions.
• Counter drift with handoffs: ask the AI to write a full handoff prompt (errors, nuances, code snippets, relevant files) as if briefing a new engineer with zero context. Paste that into a fresh session.
• Chain handoffs across sessions to preserve continuity without relying on a single long conversation.

The macro-micro loop

• The unifying principle across all three phases: always return to the macro after each micro iteration.
• Build something small → test it → verify it works → check off the step → return to the macro plan → repeat.
• This loop is what allows AI to build robust applications without losing the thread.