I recently did an experiment to see how far I could push AI code generation while strictly controlling the output quality. I wrote an article about building a functional E-commerce MVP using only 5 prompts.
The core idea I explored is what I call Requirement-Driven Development (RDD). Instead of just asking the AI to "build me a store," I structured the prompts to act as strict technical specifications (defining scope, data models, components, and state management beforehand).
I found that spending time writing a bulletproof requirement prompt drastically reduced the hallucination and "spaghetti code" AI usually spits out for complex apps.
I've detailed the exact prompts, the stack I used, and the trade-offs in the Medium article. I’m curious to know if anyone else here is adopting a similar "spec-first" approach when working with LLMs, or if you see potential pitfalls with this RDD concept at scale?
Hi HN,
I recently did an experiment to see how far I could push AI code generation while strictly controlling the output quality. I wrote an article about building a functional E-commerce MVP using only 5 prompts.
The core idea I explored is what I call Requirement-Driven Development (RDD). Instead of just asking the AI to "build me a store," I structured the prompts to act as strict technical specifications (defining scope, data models, components, and state management beforehand).
I found that spending time writing a bulletproof requirement prompt drastically reduced the hallucination and "spaghetti code" AI usually spits out for complex apps.
I've detailed the exact prompts, the stack I used, and the trade-offs in the Medium article. I’m curious to know if anyone else here is adopting a similar "spec-first" approach when working with LLMs, or if you see potential pitfalls with this RDD concept at scale?
Would love to hear your thoughts or criticisms!