flâneur

The context problem: Why enterprise AI needs more than foundation models - Stack Overflow

stackoverflow.blog · 2,362 words · saved by 1 readers

The difference between AI that impresses people in demos and AI that drives production value is context. Ask an AI coding assistant to, say, “build a React component with a dropdown menu,” and you'll probably get something impressive in seconds—clean code, proper hooks, accessible markup. It's the kind of demo that makes CTOs lean forward in their chairs. Now ask that same AI about your company's internal API for user authentication. Ask it to integrate with your legacy billing system. Ask it why your team deprecated a particular approach last quarter. Watch it hallucinate with confidence, suggesting endpoints that don't exist, recommending patterns your architecture explicitly forbids, and generally ignoring the hard-won institutional knowledge that makes your systems actually, you know, work. This is the enterprise AI paradox: Foundation models know everything about public libraries but precious little about the specifics that matter for your business. They're trained on millions of

Ask an AI coding assistant to, say, “build a React component with a dropdown menu,” and you'll probably get something impressive in seconds—clean code, proper hooks, accessible markup. It's the kind of demo that makes CTOs lean forward in their chairs. Now ask that same AI about your company's internal API for user authentication. Ask it to integrate with your legacy billing system. Ask it why your team deprecated a particular approach last quarter. Watch it hallucinate with confidence, suggesting endpoints that don't exist, recommending patterns your architecture explicitly forbids, and gener

saved by

related reading