{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Machine Learning Tech Brief By HackerNoon","title":"Behind the Curtain: Why the Most Successful AI Apps are Actually Code-First.","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/0a55f49b\"></iframe>","width":"100%","height":180,"duration":216,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/behind-the-curtain-why-the-most-successful-ai-apps-are-actually-code-first.\nWe tried an LLM-first approach for API validation and mock data. It worked in demos but failed in production. Code-first made it stable and predictable.\nCheck more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.\n            You can also check exclusive content about #ai, #llm, #software-engineering, #api-design, #open-api, #microservices, #backend-development, #llm-handles-everything,  and more.\nThis story was written by: @swapneswarsundarray. Learn more about this writer by checking @swapneswarsundarray's about page,\n            and for more stories, please visit hackernoon.com.\nWe tried letting the LLM handle everything—mock data, validation, flows. It worked in demos but failed in production with inconsistent outputs. We moved to a code-first approach where code enforces rules and LLM is used only for gaps. That made the system stable.\n        \n        ","thumbnail_url":"https://img.transistorcdn.com/KyA01h2FD2insgk-wX_xzV6vbJnTNl2BvPYVL-XaI9A/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjcyLzE2ODM1/ODI0ODgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}