MCP Mastery
About
Chapter 1
warmup
~30 min

What Is An Eval?

Stop grading one lucky demo and calling it evidence.

Eval Toolkit 2026.05
Observability trace-first
Python 3.11
Reviewed 2026-05-17

Reading this chapter helps prevent 5 common Eval Writing mistakes.

The setup

An eval is a structured argument about whether a system is ready for a specific decision. The unit is not "model quality" in the abstract. The unit is a product claim: this assistant can answer billing questions with citations, this classifier can abstain on ambiguous tickets, or this summarizer can preserve the contractual clause that matters.

Picture this

Good, bad, and ugly paths for decision-backed evaluation.

Mental model

Think of an eval as a contract with five parts: task, examples, scoring rule, threshold, and action. If any part is missing, people will fill the gap with vibes, seniority, or the last demo they remember. Great, now your launch process is a group chat.

Good

The good version names the product decision first. It says: "We will expand to 10 percent of traffic if billing-answer citation faithfulness is at least 95 percent on high-risk slices and no critical policy failures appear." The eval has a dataset version, rubric version, scorer version, and a documented owner.

Bad

The bad version runs twenty prompts, picks the best transcript, and calls the model improved. It usually has no fixed threshold and no failure taxonomy. If the answer sounds fluent, everyone relaxes, which is adorable until production users ask different questions.

Ugly

The ugly reality is that stakeholders often want one number. You may need one headline number, but it must point to slice detail, failure examples, and an action. The headline is the doorway, not the building.

Artifact to produce

Write an eval card with: decision, task boundary, dataset source, slices, scoring rule, threshold, refresh cadence, and rollback owner.

Eval card review

QuestionWhy it matters
Does the eval name the decision it informs?Without this, the result is trivia.
Are task, examples, metric, threshold, and action all explicit?Missing pieces become hallway debate.
Can another reviewer rerun the same procedure?Repeatability is the difference between evidence and storytelling.

Chapter takeaway

A first eval should make the next decision less ambiguous. If it cannot do that, it is probably a score-shaped decoration.

References

Quiz

  1. What must be defined before an eval can guide launch?

  2. Which is the bad version of decision-backed evaluation?

  3. What should the ugly reality change about your process?