Building Reliable AI Systems: Why Prompting Isn’t Enough
Introduction Most generative AI demos work. Most generative AI systems fail. That gap isn’t about model quality—it’s about system design. Over the past year, I’ve been experimenting with applying large language models to real engineering workflows—generating structured outputs from messy inputs, integrating enterprise data, and building agent-like systems. The biggest lesson so far: prompting is the easy part. Building something reliable around it is the real engineering problem. This mirrors a pattern seen in distributed and mobile systems—reliability emerges from architecture, not individual components. ...