Production LLM Systems: What CTOs Need to Know
Moving from demos to durable AI product infrastructure.
A production LLM system is not a prompt wrapped in an API. It is a product system with retrieval, permissions, evaluation, monitoring, cost controls, fallback behavior, and a workflow that real users understand.
The hard part is usually not getting a good answer once. The hard part is getting acceptable answers repeatedly across messy inputs, changing data, latency limits, and business constraints.
CTOs should ask a few grounding questions early: what source of truth does the model use, how do we know the answer is good, what happens when confidence is low, and how will engineers debug failures after launch?
Teams that answer those questions explicitly build systems that improve. Teams that skip them end up with impressive demos that are difficult to support, measure, or sell.