Evaluating LLMs for Code Generation: Accuracy, Latency, and Failure Modes

There's a moment every engineer hits when using LLMs for code: the output looks perfect… until it isn't. The function compiles, the structure feels right, but something subtle breaks under real usage. That gap between "looks correct" and "is correct" is exactly where most evaluations fail. Instead of treating LLMs like magic code generators, it's useful to treat them like distributed systems: non-deterministic, latency-sensitive, and full of edge cases. This article explores a grounded way to evaluate them - through accuracy, latency, and failure behavior - while.