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Articles tagged with #large-language-models
Nothing shipped. The git log is clean. The model is behaving differently anyway. In an LLM system, that is not a mystery. It is a versioning failure with a known architecture, and this article is that architecture.
The new model swept the golden set, the regression suite, and the LLM as judge evals. Then CSAT dropped. The eval did not lie. The eval measured exactly what it was built to measure. That was the problem.
When the interviewer says "design an ML serving platform at scale," your infra instincts will betray you on every dimension: the bottleneck is GPU memory, output length determines latency, and the batching strategy you learned for image classifiers is worse than useless here.
