Evaluation
Advanced 13 min read Evaluation

Eval Data Engineering

The datasets behind the gates

Eval datasets are a living asset: gathered from real traffic, labeled consistently, kept clean and with coverage balance. How you manage eval data determines how much you can trust the gates built on it.

eval datadatasetslabelingcoverageprivacyversioningaugmentation
Prerequisites: Golden Tests
Before we start

What you will learn

  • Source and label eval datasets
  • Balance coverage and avoid leakage
  • Govern privacy and versioning
Introduction

What is this?

Eval data engineering treats datasets as production assets: sourced from real traffic plus curated synthetics, labeled with consistent rubrics, balanced across domains and difficulty, privacy-filtered, and versioned. Leakage (test data seen in training or in prompts) and label drift silently corrupt the gates.

Motivation

Why do you need it?

  • Gates are only as trustworthy as the data they enforce.
  • Coverage gaps create blind spots exactly where failures live.
  • Leakage produces falsely green suites — the worst lie.
  • Versioned, governed datasets survive team turnover and product churn.
Think about it

Mental model

Imagine

A biobank's sample collection

Think of

Eval dataset

Samples (cases) collected with consent (privacy), labeled with provenance (source, date), balanced across populations (coverage), and versioned in freezers (versioning). Contaminated or mislabeled samples poison every study. Your golden repository needs the same care.

Mechanics

How it works

  • Harvest real cases + author curated edges.
  • Label with a shared rubric and inter-rater checks.
  • Balance domains, difficulty, and modalities.
  • Screen for leakage and PII; scrub where needed.
  • Version datasets; snapshot provenance per case.

Dataset pipeline.

Harvest
Label
Balance
Guard
Version
Real world

In production

  • Golden repos version like code with review on add.
  • Coverage dashboards flag under-tested domains.
  • PII scrubbers run before any dataset ships.
Guidelines

Best practices

  • Attach provenance (source, date, author) to every case.
  • Run leakage scans (overlap with training sets and prompts).
  • Keep coverage matrix visible; fill blind spots deliberately.
  • PII: anonymize or exclude before the dataset is shared.
Watch out

Common mistakes

  • Test cases that appear in training → false green.
  • Label drifting across reviewers over time.
  • One giant dataset with no domain or difficulty labels.
Root cause analysis

Common issues

Error
Data contaminated with PII
Root cause

Scrubbers skipped

Fix

Automated + manual PII review gate before publishing.

Error
Dataset stale against product
Root cause

Old distribution

Fix

Refresh cadence with live harvests and retirement rules.

When things break

Debugging tips

  • Suite too green → suspicion: leakage check + difficulty audit.
  • Failures cluster in one patch → coverage matrix shows the gap.
  • Label disputes → inter-rater sample; recalibrate rubrics.
Interviews

Interview checkpoint

Practice

Hands-on lab

Curate & Balance an Eval Dataset
35 min · advanced
In progress
Objectives
  • Version and provenance a dataset
  • Build a coverage matrix
  • Scan for leakage and duplicates

Curate & Balance an Eval Dataset

35 min · advanced

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Test yourself

Quick quiz

0/2
1

Leakage in eval data produces…

2

Eval datasets need versioning and provenance.

Go deeper

Official documentation

Human evals (OpenAI)
docs