Week 6: MCP, Evaluation, and LLMOps

LESSON

Evaluation, Tracing, and Token Accountability

If you only inspect outcomes manually, you are flying blind.

advanced 65 minutes LLMOps Gate

Objective

Design evaluation and tracing practices that let you monitor quality, cost, and failure patterns.

The lesson is public. The pressure loop lives inside the app where submissions, revision, and review happen.

Deliverable

An evaluation scorecard and post-launch monitoring plan.

Each lesson contributes to a week-level artifact and eventually to the shipped AI-native SaaS.

Preview

Public lesson preview.

Lesson Preview

Evaluation, Tracing, and Token Accountability

If you only inspect outcomes manually, you are flying blind.

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.

What This Is

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

Why This Matters in Production

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

Mental Model

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

Deep Dive

A mature AI system records enough context to reconstruct requests, prompts, retrieved evidence, tool calls, and outcomes without leaking inappropriate data. Evaluation scorecards turn vague “seems better” language into explicit axes such as factuality, rubric adherence, latency, and revision rate. Token and cost tracking matter because product viability depends on the economics of the interaction, not only its elegance.

Worked Example

A review model starts producing overly harsh feedback after a prompt revision. Traces reveal the new prompt version, evaluation scorecards reveal a drop in helpfulness, and cost metrics reveal the change also increased output tokens unnecessarily.

Common Failure Modes

Common failures include collecting traces with no retrieval or prompt version, manually eyeballing a few examples instead of defining a test set, and ignoring token cost until the bill arrives.

References

Evaluation, Tracing, and Token Accountability

Public lesson preview.

Evaluation, Tracing, and Token Accountability

Further reading the machine expects you to use properly.

LangSmith Observability

OpenAI Evals

Weights & Biases MLOps