Eval Dataset
An eval dataset is the curated set of test cases — inputs with expected outcomes — that an LLM application's quality is measured against.
An eval dataset is the fixed, curated collection of test cases an LLM feature is scored against — each case an input plus its expected outcome (a reference answer, a rubric, a constraint) — the foundation every other evaluation machinery stands on.
It's the LLM era's test suite, with one difference from unit tests: outcomes are often judged (by LLM-as-judge or humans against rubrics) rather than exact-matched, which makes case quality even more load-bearing — a vague expected outcome produces a meaningless score. The curation discipline: mine real traffic for the head of the distribution, promote every production failure into a permanent regression case, and synthesize the edge cases your logs haven't produced yet — keeping the dataset versioned, since changing it silently breaks score comparability.
Its strategic role is bigger than testing: the eval dataset is where a team's definition of good becomes executable — the artifact that turns "the new prompt feels better" into a number that gates releases. Building one from zero is the first half of Write Evals for an LLM App, scaffoldable via the llm-eval-suite-scaffolder skill.
Frequently asked questions
- How big does an eval dataset need to be?
- Smaller than people fear, better-curated than people bother: 50–200 well-chosen cases beat 5,000 random ones. What matters is coverage — typical cases, known edge cases, past failures (every production bug becomes a case), and adversarial inputs — plus stable expected outcomes so scores mean the same thing run to run.
- Where do eval cases come from?
- Three sources, in priority order: real usage (mined from logs — the distribution you actually face), failures (every bug report is a regression case), and synthesis (LLM-generated variations and edge cases to fill coverage gaps you haven't hit yet — generated cheaply, curated ruthlessly).
Related
- Write Evals for an LLM App: From Zero to a CI GateHow to evaluate an LLM feature — build a dataset, choose metrics, set a baseline, score offline, add an LLM judge, and gate CI so quality changes are measured.
- LLM-as-JudgeLLM-as-judge uses a language model to score AI outputs against a rubric — evaluating quality at scale where exact-match metrics fail and humans don't scale.
- Synthetic DataSynthetic data is training or eval data generated by a model rather than collected from the world — filling gaps, balancing classes, bootstrapping fine-tunes.
- LLM Eval Suite ScaffolderStand up an evaluation suite for an LLM feature from scratch — a representative dataset, the right metrics, a baseline score, and a CI gate — using DeepEval, promptfoo, or RAGAS. Use when a feature has no evals, before tuning a prompt, or when adding an LLM feature to CI.
- Best LLM & RAG Evaluation Tools in 2026: DeepEval vs RAGAS vs LangSmith vs Phoenix vs promptfooA decision guide to the LLM eval landscape — code-first frameworks vs. eval-and-observability platforms, open-source vs. hosted, and which fits your stack.