Building Evaluation Datasets

Your evaluation is only as good as your dataset. This article covers the complete lifecycle of building eval datasets: manually curating golden sets, generating synthetic data with stronger models, crafting adversarial examples, avoiding contamination, and determining the right dataset size for statistical significance.

Quick Reference

→Golden datasets: manually curated, high-quality question-answer pairs — the bedrock of evaluation
→Synthetic generation uses a stronger model (e.g., GPT-5.4) to create eval data at scale
→Adversarial examples expose edge cases — typos, ambiguity, prompt injection attempts, boundary conditions
→Contamination: never let eval data leak into training / prompt examples / few-shot sets
→For 95% confidence with 5% margin of error, you need ~385 eval samples minimum
→Refresh eval datasets quarterly with real production queries to combat distribution shift

Golden Datasets: The Bedrock of Evaluation

A golden dataset is a manually curated collection of input-output pairs where the expected output has been verified by domain experts. This is the most trustworthy form of evaluation data because every answer has been human-validated. The downside is cost: creating golden datasets is slow, expensive, and does not scale. But you need them — they are the calibration standard against which all other evaluation methods are benchmarked.

Start with 50 golden examples

You do not need thousands of golden examples to start. 50 carefully curated examples covering your main use cases give you enough signal to catch major regressions. Grow to 200-500 as your system matures. Quality of annotations matters far more than quantity.

Golden dataset structure with rich metadata for traceability

▸Include metadata for every example: who annotated it, when, what source material they used
▸Categorize examples by type (factual, reasoning, creative, safety) and difficulty to analyze performance slices
▸Version your golden datasets in git — they are as important as your code
▸Have at least two annotators independently verify each example to catch annotation errors

Synthetic Generation: Scaling with Stronger Models

Golden datasets do not scale. Creating 50 examples by hand is feasible; creating 5,000 is not. Synthetic generation uses a stronger model to create eval data programmatically. The key insight is that generating eval data is easier than generating correct answers — a model can create plausible questions from documents more reliably than it can answer arbitrary questions.

Adversarial Examples: Breaking Your System on Purpose

If your eval dataset only contains well-formed, reasonable questions, you are testing the happy path. Adversarial examples deliberately probe the boundaries of your system — malformed inputs, edge cases, and attack vectors. They are the difference between a system that works in demos and a system that survives production.

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