The architectural trade-offs of AI code generation

Building an AI tool that generates code requires careful architectural choices that differ from typical human-developer considerations. The post examines key trade-offs: Ruby's token efficiency vs. TypeScript's type safety, Rails' convention-over-configuration vs. JavaScript's flexibility, mature library ecosystems vs. cutting-edge AI tooling, and bridging a conventional backend to a modern React frontend. The author argues that Ruby on Rails' opinionated conventions, conciseness, and mature libraries make it better suited for LLM-generated code than TypeScript, despite TypeScript's advantages in type safety and unified full-stack context. The post is written in the context of building Aha! Builder, an AI-powered app generation product.