Lesson 1 · 8 topics · 22 min · intermediate

Success Criteria: Deciding What Good Means

You will be able to

Explain evaluation-driven development: write down how an application will be evaluated before building it, and why a deployed system nobody can evaluate is a liability.
Sort an application's criteria into domain capability, generation, instruction following, and cost and latency, and say why they're scored separately.
Write an evaluation guideline that says what good means, including what the application must not do and how out-of-scope inputs are handled.
Turn each criterion into a rubric with a scoring system and examples, and check it by having two people grade the same outputs.
Tie a quality score to a business outcome, and set a usefulness threshold.
Score each component and each task, not only the final output, so a failure points at what to fix.
Slice results and recognise Simpson's paradox, and ask whether the evaluation itself gives a reliable, affordable signal.

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Sources

AI Engineering · Chip Huyen, 2025
Chip Huyen. O'Reilly. Used: Ch. 4 "Evaluate AI Systems": "Evaluation Criteria" (including "Evaluation-driven development"); "Design Your Evaluation Pipeline" > "Step 1. Evaluate All Components in a System"; "Step 2. Create an Evaluation Guideline" > "Define evaluation criteria", "Create scoring rubrics with examples", "Tie evaluation metrics to business metrics"; "Step 3. Define Evaluation Methods and Data" > "Select evaluation methods", "Annotate evaluation data", "Evaluate your evaluation pipeline"; "Iterate".

You will be able to

Explain evaluation-driven development: write down how an application will be evaluated before building it, and why a deployed system nobody can evaluate is a liability.

Sort an application's criteria into domain capability, generation, instruction following, and cost and latency, and say why they're scored separately.

Write an evaluation guideline that says what good means, including what the application must not do and how out-of-scope inputs are handled.

Turn each criterion into a rubric with a scoring system and examples, and check it by having two people grade the same outputs.

Tie a quality score to a business outcome, and set a usefulness threshold.

Score each component and each task, not only the final output, so a failure points at what to fix.

Slice results and recognise Simpson's paradox, and ask whether the evaluation itself gives a reliable, affordable signal.

Sources

AI Engineering · Chip Huyen, 2025

Chip Huyen. O'Reilly. Used: Ch. 4 "Evaluate AI Systems": "Evaluation Criteria" (including "Evaluation-driven development"); "Design Your Evaluation Pipeline" > "Step 1. Evaluate All Components in a System"; "Step 2. Create an Evaluation Guideline" > "Define evaluation criteria", "Create scoring rubrics with examples", "Tie evaluation metrics to business metrics"; "Step 3. Define Evaluation Methods and Data" > "Select evaluation methods", "Annotate evaluation data", "Evaluate your evaluation pipeline"; "Iterate".