Build More Apps Faster with AI: A Developer's Pitfall Guide

Executive overview

AI coding tools have fundamentally shifted the effort-to-output ratio for software development. A developer who built more apps in six months than in the previous six years walks through the exact workflow and common failure modes. The approach centres on structured planning documents, phased implementation, and using multiple AI tools for their distinct strengths.

Proper upfront specification combined with phased prompting eliminates the majority of AI coding errors before they occur.

The AI vs. traditional development curve

• Traditional development follows a boom-bust cycle: excitement, bug, research, fix, repeat — often ending in a frustration death spiral.
• AI-assisted development front-loads progress: code is written for you, errors are fixed faster, and projects complete in a fraction of the time.
• Non-engineers can now ship real applications — the barrier is specification, not coding skill.

AI coding tool landscape

• Replit Agent and Bolt.new target no-code users; closest replacements for Wix/Squarespace, browser-based with built-in deploy.
• v0 specialises in aesthetically polished UI generation.
• GitHub Copilot is the enterprise incumbent from Microsoft.
• Cursor and Windsurf are the power-user tools — more features, more control, preferred by serious AI builders.

The planning workflow (before touching Cursor)

• Describe the end-state application in full detail, then send to GPT-4o with a prompt asking it to expand the idea as if explaining to a non-engineer.
• Feed that expanded description into O1 and request a product requirements document (PRD) in Markdown — phased, with checkboxes per implementation step, written as if for a new engineer with no context.
• The resulting PRD becomes the single source of truth fed into all subsequent model interactions.

Cursor tips and tricks

• Use the latest Claude 3.5 Sonnet (dated version) — Claude outperforms alternatives on coding tasks.
• Use Composer (not Chat) — it has access to all project files and broader context.
• Switch Composer to Agent mode for the most capable execution.
• Start each session by asking the model to review the full PRD before doing anything.
• Build one phase at a time: complete a phase, have the model check off its boxes in the PRD, then move to the next.
• Add API documentation via Cursor's Docs feature (Settings → Features → Docs) — paste a URL, Cursor scrapes the entire doc set into its knowledge base for live reference.

Debugging tactics

• Test-driven development: add a step in the PRD phase requiring the model to write a test for a function before implementing it; the function must pass before moving on.
• Print-statement debugging: when a recurring error appears, ask the model to insert print statements throughout the relevant function to surface more terminal output, then feed that output back into the model.
• Ask a friend (O1 escalation): when an error recurs multiple times, paste the full error context and terminal output into O1 with a note that it has happened repeatedly; take O1's suggested alternative approach back into Cursor and ask Claude whether the solution is sound before implementing.

Cursor vs. Windsurf — use them together

• Cursor's advantage: the Docs scraping feature keeps the model grounded in up-to-date API references.
• Windsurf's advantage: superior whole-project context; it can analyse all files and identify dead code that can safely be deleted — Cursor is weaker here.
• Practical workflow: build in Cursor, use Windsurf for end-of-project cleanup and cross-file analysis.

Where this is heading

• As models improve (GPT-5, Claude 4 etc.), the structured PRD workflow will become unnecessary — simpler prompts will produce more complete results.
• Future interaction model: the AI prompts the developer, not the other way around — surfacing ambiguities and requesting decisions rather than guessing.