LLMOps Basics
Learn the operating discipline for LLM products: evaluation, monitoring, release management, feedback loops, and cost controls.
8 min
LLMOps is the practice of shipping and improving LLM-powered systems without losing control of quality, safety, cost, or user trust.
What to Monitor
LLM products need traditional software observability plus AI-specific signals.
| Signal | Why It Matters |
|---|---|
| Latency | Users abandon slow workflows |
| Cost | Model and retrieval costs can scale quickly |
| Error rate | Tool failures and validation failures break trust |
| Quality score | Prompt or model changes can regress silently |
| Retrieval health | Bad context causes bad answers |
| User feedback | Real workflows reveal missed cases |
Evaluation Sets
An evaluation set is a collection of representative inputs, expected traits, and scoring rules. Keep examples for common cases, edge cases, and high-risk cases.
type EvalCase = {
input: string;
expectedTraits: string[];
mustNotContain: string[];
sourceIds?: string[];
};
Release Management
Treat prompt, retrieval, and model changes like product releases. Use versioning, staged rollout, monitoring, rollback criteria, and changelogs.
If you cannot explain what changed and how you measured it, the release is not ready.
Feedback Loops
Feedback is only useful when it reaches the system. Tag failures, inspect examples, update prompts or retrieval, add eval cases, and measure again.
Next Step
Take the LLMOps quiz, then create a release checklist for one AI feature you want to ship.
Practice this topic
Reinforce the concepts from this lesson with a short quiz and explanation review.
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