Introduction to LLMOps
Learn the operational practices that turn an LLM prototype into a dependable product.
Why LLMOps exists
Traditional software expects deterministic code paths, while LLM applications include probabilistic outputs, changing provider behavior, prompts, retrieval, and tool calls. LLMOps applies engineering discipline to those moving parts. It connects experimentation with release management, evaluation, monitoring, cost control, security, and incident response so a successful demo can become a dependable service.
The application lifecycle
An LLM feature moves through data preparation, prompt design, model selection, evaluation, deployment, monitoring, feedback, and revision. Each stage can change the result seen by users. Keep the stages connected with versioned artifacts and trace identifiers. A prompt experiment that is not linked to its evaluation or production outcome is difficult to learn from and easy to repeat accidentally.
release:
model: support-assistant-v3
prompt_revision: 18
evaluation_set: support-held-out-2026-07
rollout: canary-10-percent
rollback_on:
- error_rate_gt_2_percent
- groundedness_lt_0.9What to record
Record model and provider, prompt version, retrieval configuration, tool definitions, token counts, latency, validation results, safety signals, and user feedback. Redact or hash personal data before traces are stored. Sampling can reduce volume, but never sample away the failures needed for debugging. Define retention and access rules alongside observability so monitoring does not become a new data leak.
Quality as a production signal
Availability and latency are necessary but not sufficient. Track groundedness, schema validity, refusal behavior, user correction, task completion, and escalation rates. Some quality signals arrive later through reviews or support tickets, so connect them back to the original trace. A drop in quality may come from a model update, retrieval change, prompt edit, or input distribution shift.
Change management
Treat prompts, model IDs, examples, tools, schemas, and retrieval settings as releaseable assets. Use code review, version labels, regression tests, staged rollout, and rollback plans. A seemingly harmless wording change can alter tool selection or expose information. Make the complete configuration visible in a deployment record rather than relying on memory or a dashboard screenshot.
Failure handling
Provider errors, timeouts, malformed outputs, rate limits, empty retrieval, and unsafe responses need explicit paths. Use deadlines, bounded retries, fallbacks, circuit breakers, and human escalation where appropriate. Do not silently replace a failed answer with a confident guess. Record the failure category and user-visible behavior so reliability work can prioritize the problems that matter most.
Starting small
Choose one production workflow and make its traces, evaluation set, cost, and failure modes visible. Add a release checklist and a simple dashboard before introducing a complex platform. The goal is repeatable learning: every model or prompt change should show whether the real task became better, cheaper, faster, and safer. Expand the process as the number of AI features grows.