LLMOps Foundations

Course overview

Foundations of LLMOps and AI observability: differentiate MLOps, LLMOps and app observability, map the AI lifecycle (prompt, retrieval, tools, model, output, feedback), and set SLOs for quality, latency, cost and safety using logs, traces and metrics.

Clarifies how LLMOps differs from traditional MLOps and general app observability.

Details the AI lifecycle: prompt engineering, retrieval, tool integration, model inference, output handling and feedback loops.

Introduces SLO design that treats quality, latency, cost and safety as measurable dimensions.

Explains how logs, traces and metrics complement each other to monitor and debug AI systems.