LLMOps
The operational practices for deploying, monitoring, and managing large language models in production.
Definition
The tools, practices, and processes for operationalizing applications built on large language models. It extends MLOps to the specific challenges of LLMs: managing and versioning prompts, tracking token costs, monitoring latency and output quality, running evaluations to catch regressions, and enforcing safety guardrails. It also covers retrieval pipelines, caching, and fallback logic for when a model is slow or unavailable.
Why it matters
Building an LLM demo is easy; running one reliably and cost-effectively in production is not. LLMOps discipline is what prevents runaway API bills, silent quality drops, and unmonitored failures, and it is what lets a team ship LLM features they can actually trust and maintain.
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From vocabulary to outcomes
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