Week 6: MCP, Evaluation, and LLMOps
MCP, tracing, evals, cost, compliance-aware logging, and post-launch operations.
What the machine expects from you.
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.
Three dense lessons, one enforced deliverable.
What MCP Is and Why It Matters
MCP is a tool boundary and integration contract, not a trend checkbox.
LessonEvaluation, Tracing, and Token Accountability
If you only inspect outcomes manually, you are flying blind.
LessonPII Masking, Audits, and Post-Market Monitoring
AI systems require care after launch, not just before launch.
What survives the week.
AI Evaluation Scorecard
A rubric-driven eval scorecard for quality, cost, and failure monitoring.
An evaluation scorecard and post-launch monitoring plan.
Each week leaves behind portfolio evidence that compounds into the final SaaS and its operating narrative.