Why LLM features need a different ops model
Traditional ML pipelines assume relatively stable models and features; LLM systems change behavior with prompts, tools, retrieval corpora, and upstream API versions.
Teams that treat LLM releases like static API deploys often see quality regressions discovered only through user complaints.
MLOps for LLMs must combine software delivery discipline with continuous evaluation of semantic outputs.
Reference pipeline: build, eval, release
A practical pipeline includes dataset snapshots for evals, blocked releases on regression thresholds, staged rollouts, and rollback paths for prompts and retrieval indexes.
Prompt and tool configurations should be versioned like application code, with immutable artifacts referenced at deploy time.
Infrastructure should separate batch indexing jobs from online inference so retrieval updates do not silently change behavior without review.
Operating LLM systems in production
Runbooks should cover model provider outages, retrieval latency spikes, and toxic or policy-violating outputs with clear escalation paths.
Cost controls include caching, model routing, and budget alerts tied to product surfaces — not only monthly invoices.
Mature teams review eval drift weekly and tie roadmap work to the highest-impact failure clusters.