Inefficient Workflow in Using Language Models for Product Development

I went from letting cool ideas either die in my notes app or become another project I never had time to finish to having 4 products live, with 2 already making MRR. Claude and Codex went from feeling like frustrating slop cannons that constantly left features half finished and disconnected to actually feeling useful. Most people build by throwing giant poorly scoped prompts at models and hoping the app somehow comes together. Then 3 days after launch the auth is broken, Stripe barely works, the frontend isn’t wired correctly, and the whole thing feels fragile or has hidden security issues. The models aren’t the problem. The workflow is. So I built an intelligence layer for any LLM I choose to use called LaunchChair to rapidly spin products up around actual market gaps and wedges instead of random ideas that sounded cool for 20 minutes. I can go from idea to scoped MVP, generate feature-by-feature build prompts with full context already attached, and move through builds without constantly rewriting prompts or re-explaining the app every 5 seconds. Each prompt has strict agent contracts, feature scope, spec context, and guardrails so your LLM doesn’t end up reading your entire codebase every prompt, you use less tokens for a project and the code output is dramatically better. Each build card even automatically generates a remediation prompt if your LLM doesn’t pass acceptance criteria for each feature. The difference now is I can build fast without the product feeling disposable. Features connect properly, flows make sense, the product direction stays coherent, and I’m not wasting half my time recovering broken context windows or untangling AI spaghetti code. Shipping stopped feeling exhausting once the process itself was more inline with how an actual product team works. I can spin up an MVP in a few days now and it doesn’t feel like vibe slop, has better structure, security, and infrastructure that will actually scale.