AI Pipeline: Idea to Deploy
End-to-end pipeline for building and shipping a SaaS app using AI. Covers strategic planning → product ownership → research → ideation → coding → review → testing → deployment. Based on real workflows from solopreneurs shipping in 1-2 weeks.
Step-by-Step Guide
Phase 1 — Strategic Planning (Best LLM: ChatGPT o3)
Define what you're building and why. Ask ChatGPT: "I want to build [idea]. Help me define the MVP scope, target users, monetization model, and key metrics." Use the LLM as a product strategist — challenge assumptions, identify risks.
Pro tip: Ask for a Lean Canvas output format. This structures your thinking and catches gaps.
Phase 2 — Product Ownership (Best LLM: ChatGPT + Claude)
Turn strategy into tickets. Use ChatGPT to break the MVP into epics and user stories. Then use Claude to review the product spec for edge cases and missing features. Create a backlog of 10-15 tickets for your first sprint.
Pro tip: Output as markdown files in your project repo. Cursor can read these directly.
Phase 3 — Research (Best LLM: Perplexity + Gemini)
Research your tech stack, competitors, and best practices. Use Perplexity for up-to-date documentation and API references. Use Gemini for deep-dive research into architecture patterns and edge cases.
Phase 4 — Ideation & Architecture (Best LLM: Claude)
Design your system architecture. Claude excels here — it understands the full picture. Ask for: data model, API routes, component tree, and state management plan. Review and iterate before writing code.
Pro tip: Ask Claude to generate a PRD (Product Requirements Document) — this becomes your blueprint.
Phase 5 — Coding (Best LLM: Cursor + Claude)
Start with Cursor's Agent mode. Provide your PRD + tickets and let it scaffold the project. For complex logic, switch to Claude for implementation. Use Cursor's Composer for multi-file changes.
Phase 6 — Code Review (Best LLM: Claude)
After each feature, paste the diff into Claude. Ask: "Review this for bugs, security issues, performance problems, and edge cases." Claude catches things human reviewers miss.
Phase 7 — Testing (Best LLM: ChatGPT + Devin)
Generate test suites with ChatGPT. For E2E testing, describe your user flows and let it write Playwright/Cypress tests. Devin can autonomously run and fix failing tests.
Phase 8 — Deployment (Best LLM: ChatGPT)
Use ChatGPT for DevOps: Dockerfile generation, CI/CD configuration, environment variable management. Deploy with Vercel for frontend, Railway/Render for backend. Let ChatGPT debug deployment errors.
Pro tip: Ask ChatGPT to write a deployment checklist — you'll follow it in <30 min.
Pro Tips
Keep a project-level markdown file with all decisions — feed it to every new LLM session for context
The full pipeline from idea to deploy takes 1-2 weeks for a solo dev using this workflow
Don't skip Phase 1-4 — 80% of projects fail because of poor planning, not bad code
Record your MVP build as content — "Building in 2 weeks with AI" is a top-performing format
Common Mistakes to Avoid
Mistake: Jumping straight to coding without architecture planning
Fix: Spend 2 hours on phases 1-4. It will save you 20+ hours of refactoring later.
Mistake: Using one LLM for all pipeline stages
Fix: Each stage has a best-fit LLM. Use the recommendations above. The cost difference is negligible vs. the quality gain.
Tools in this Playbook
Browse all toolsChatGPT
The most versatile AI assistant for daily tasks
Cursor
AI-native code editor built for productivity
Claude
Thoughtful AI for complex reasoning and long documents
Perplexity
AI-powered research engine with cited answers
Canva AI (Magic Studio)
AI-powered design for non-designers