Production engineering for LLM systems. Evaluation pipelines, online observability, cost and latency tradeoffs, prompt-version drift, A/B on real traffic, and the cases where the LLM-stack hype crashes into the operational reality.
A practitioner's guide to the LLMOps best practices that separate fragile demos from reliable production systems: prompt versioning, observability, evaluation, and cost governance.
What the hype skips about model registries, what mature teams actually do, and how to avoid the metadata graveyard most registries become.
Most LLM apps track total spend and call it done. The interesting signals — per-feature cost, per-user attribution, anomaly bands — require deliberate instrumentation.
How training/serving skew happens, why it's so hard to see, and the specific places to look when your model works in eval and breaks in prod.
Operating LLMs in production — eval, observability, cost, latency. — delivered when there's something worth your inbox.
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