Putting LLMs into production safely

A demo that works once is not a product. The gap between the two is evaluation: a repeatable way to know whether a change made things better or worse.

Build the eval harness before the feature. Curate real examples, define what good looks like, and score every change against it. This is what lets you move fast without breaking trust.

Add guardrails at the edges — input validation, output checks, and fallbacks — so the system fails safely. Then watch cost like a hawk; the cheapest token is the one you didn't need to send.