LLMOPS

Shipping a language model application is not like shipping a web service. The artifact being deployed is probabilistic. The same prompt produces different outputs on different days. The same model behaves differently across providers and regions. A change that improves your evaluation metrics on Tuesday can quietly regress them by Friday as production traffic shifts underneath you. Traditional DevOps has no answer for any of this. LLMOps is a practical engineering guide to operating large language model applications in production. It starts from a simple observation: the discipline most teams need is not model training, and it is not prompt engineering. It is the operational layer that keeps a probabilistic system honest — versioned prompts, layered evaluation, continuous monitoring, cost attribution, reversible deploys, and the incident playbooks that turn each failure into an improvement. The book walks through the full operational stack. It treats prompts as versioned products with their own registry, schema, and release process. It covers evaluation in depth: offline golden sets, online proxy signals, continuous regression gates, and the LLM-as-judge pattern that makes automated scoring practical — including the bias analysis and calibration work required before a judge can be trusted. It covers monitoring for the failures that do not produce error codes, distributed tracing across multi-step request chains, cost engineering and token economics, latency engineering and semantic caching, and CI/CD pipelines built for an artifact that is partly code and partly natural language. It covers the operational realities most teams learn the hard way. A prompt change that improves offline metrics and regresses on live traffic. A model provider deprecating a version with thirty days notice. A cost anomaly that triples the monthly bill before anyone notices. A cache that serves stale results across a prompt version boundary. A guardrail that blocks legitimate requests while missing obvious abuse. Each is presented with the failure, the countermeasure, and the operational tradeoff that comes with it. Fourteen chapters. Real Python code. Written for engineers and platform teams who need language model systems to work at scale, not in a demo.