Early in their careers, engineers realize they can’t learn everything and often choose specialties like backend, frontend, or DevOps. Despite this specialization, there’s foundational knowledge that all engineers share, such as database management and cloud integration. However, the rise of generative AI applications often relies on “vibe coding,” a method that lacks the rigorous structure needed for production-level software. Many founders get caught up in perfecting prompts for demos, only for their apps to falter when facing real-world data complexities in production.
Building reliable AI features requires a comprehensive ecosystem including evaluation systems, continuous optimization, memory, observability, and integration with existing data. These aspects ensure applications perform consistently and can adapt to user needs. To succeed, companies must not treat AI as a casual experiment but apply standard software engineering principles. The key question for founders is whether they are building a demo or a robust system, emphasizing the importance of structured engineering over improvisation.
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