Week 6: MCP, Evaluation, and LLMOps

This lesson introduces MCP as a structured way for models to interact with external capabilities through explicit contracts.

Without a clean tool boundary, model integrations become one-off hacks with inconsistent semantics, poor auditability, and weak control over capabilities.

MCP is not the product. It is the boundary layer between model reasoning and external tools or context providers. Good boundaries make systems composable and governable.

This lesson teaches how to make AI system quality visible through traces, evals, and cost-aware instrumentation.

Without structured evaluation, teams mistake vivid examples for real quality. Without traces and cost telemetry, they cannot explain regressions or runaway spend.

Observability answers what happened. Evaluation answers whether it was good. Cost accountability answers whether it was worth it.

This lesson brings governance into the operating loop: handling sensitive data, preserving auditability, and defining what to watch after launch.

Many teams think launch is the finish line. For AI products, launch is the beginning of continuous evidence collection about drift, misuse, and user harm.

Post-market monitoring means you assume the system will surprise you. Your job is to create the telemetry, review loop, and intervention paths needed when it does.