Offline evaluation datasets: Curating test sets

Great evals rarely start with a bigger model. They start with a sharper test set. The quickest way to ship confidently is to turn offline evals into a high-signal safety net that mirrors the product, not a lab fantasy.

This post shows how to do that. It covers what to include in your test sets, how to label and grade consistently, and how to keep everything fresh as your product changes. Along the way, it pulls in practices from CD4ML, experimentation, and RAG-heavy systems, with links to concrete guides and tools.

The impact of careful test curation

Strong offline evals are opinionated: they reflect your users, your failure modes, and your release cadence. That is how they catch regressions quickly and cheaply. Statsig’s guide to offline evals walks through framing and automation if you need a starting point docs.statsig.com.

Align the test set to delivery loops so dev environments don’t drift from production. Martin Fowler’s CD4ML work is a useful compass for this kind of tight integration between data, code, and deployment martinfowler.com. And since AI products evolve under real traffic, treat online experimentation as a first-class partner to offline evals, not a replacement. Statsig explains why experimentation is essential for AI shipping safety and speed statsig.com.

Here is what to vary on purpose:

• User intent: task types, goals, and expected tone

• Domain shift: new jargon, seasonal content, and policy changes

• Data quality: typos, HTML noise, screenshots, voice notes, and mixed languages

Push on stress inputs too. Use adversarial phrasing like “ignore previous instructions” and include rare entities, tricky spellings, and ambiguous abbreviations. For retrieval-heavy apps, design tests that isolate retrieval from generation and review practical RAG evaluation tips from the community reddit.com. Off-the-shelf benchmarks are convenient, but they often mislead for custom domains. A better path is a custom evaluation built from your own incidents and data quirks reddit.com.

As difficulty climbs, add hard negatives and distractors to expose limits. There is useful tooling emerging to generate tougher eval items if you need a boost reddit.com. Also prune relentlessly. Cutting irrelevant rows raises metric precision and reduces noise. Data engineering teams do this for curated datasets for the same reason: more signal from less data reddit.com.

Finally, ground labels to your corpus. Even sentiment words swing by domain, as shown in the Yelp analysis by David Robinson varianceexplained.org. For NER-style checks, use structured targets and strict formats so graders aren’t guessing reddit.com. The broader AI evals overview from Statsig covers flexible scoring approaches if you are stitching multiple graders together docs.statsig.com.

Structured approaches to dataset assembly

Guesswork is the enemy here. Lock in a steady path to build, verify, and keep your test set representative.

Use this playbook:

1. Start from reality: combine domain facts with past tickets, chats, and incident docs. Pull real failure modes and avoid cherry-picking. For custom domains, adapt the community guidance on building tailored RAG benchmarks reddit.com.

2. Group by critical features: intent, language, recency, channel, user tier. Track counts per bucket so coverage is visible.

3. Cover edge cases: cold start, long context, policy redactions, noisy input, chain-of-thought suppression when needed.

4. Map examples to lifecycle checkpoints from CD4ML so tests run at PR, pre-prod, and post-deploy gates martinfowler.com.

5. Split retrieval and generation: measure retrieval latency and recall for RAG, then grade answer quality separately. Community threads outline slow paths to watch and how to score them reddit.com.

6. Raise difficulty deliberately: add distractors, time-sensitive facts, and near-duplicate passages. Tooling can help generate harder prompts and negatives reddit.com.

7. Calibrate an LLM judge with small human spot checks. Use humans to establish baseline labels, then train or tune the judge to match.

8. Standardize schemas: keep consistent fields across sources and avoid one giant junk table. Curated datasets exist for a reason reddit.com.

9. Add simple gates: track feature coverage over time and block releases that drop below thresholds.

Keep offline evals tight with known answers where possible. When judgment is subjective, write it down in the rubric and include a short real example for each rule. Statsig’s offline evals workflows make it easy to version datasets, prompts, and graders side by side so results stay comparable across releases docs.statsig.com.

Effective data labeling and grading methods

Ambiguity is expensive. Clear, shared rubrics turn messy outputs into repeatable scores tied to product goals.

Build the rubric with:

• Scope and success criteria: what “good” means for this task

• Scales and thresholds: pass-fail or 1-5, and where the bar sits

• Fallback behavior: what counts as safe output when perfect isn’t possible

• Atomic labels: avoid compound judgments that hide failure modes

• Edge case examples: one or two real snippets per rule

Blend automated checks with human review for tough calls. Exact or fuzzy string match can clear the easy cases. Semantic similarity helps for paraphrases. When nuance matters, use an LLM judge backed by a strong rubric and a small human-calibrated set. Statsig’s AI evals overview lists common scoring patterns and how to combine them safely docs.statsig.com.

For repeatability, fix both the test set and the grader version:

1. Version prompts, graders, and rubrics together in the repo.

2. Pin model versions or capture model fingerprints.

3. Run the exact same grader every time. If the grader changes, bump the version and keep both results.

4. Keep labels tied to context. As seen in the Yelp sentiment work, lexicons shift by corpus, so sentiment or toxicity rules must reflect your data varianceexplained.org.

5. Where structure matters, prefer strict formats for NER and extraction tasks to avoid slippery grading reddit.com.

Treat evals like code plus data plus models. CD4ML practices make this easier to keep stable across releases martinfowler.com. Then connect offline checks to online ones so changes ship through safe experiments, not hunches. Statsig outlines that loop and why it matters for AI systems that learn under real traffic statsig.com.

Maintaining an evolving offline evaluation routine

Test sets are not static. Requirements shift, content changes, and users do surprising things. Keep the routine light but consistent so quality doesn’t drift.

A simple rhythm works:

• Cadence: monthly for volatile tasks; quarterly for stable ones

• Rotation: retire trivial items; add fresh samples from recent logs

• Versioning: bump dataset and grader versions together so comparisons stay fair

• Stress: sprinkle in synthetic stress prompts from time to time reddit.com

• Drift: watch for data drift with small, real-time pulls from production pipelines reddit.com

For RAG systems, score retrieval and answers separately so you know where the bottleneck is. The community has solid, practical tips for measuring recall, latency, and context quality reddit.com. Close the loop by pulling online misses back into offline evals. Then ship changes behind experiments that validate the gains, as the Statsig team argues for AI products that evolve under real usage statsig.com. Statsig’s offline evals docs also show how to keep datasets and graders versioned so results remain comparable over time docs.statsig.com.

Closing thoughts

Tight, curated tests beat bigger test sets every time. Build from real failures, structure the data, lock in crisp rubrics, and keep the routine humming with small updates. Split retrieval from generation when RAG is involved. Then connect offline judgment to online experiments so quality improvements really show up for users.

Want to dig deeper? Check out CD4ML by Martin Fowler for delivery patterns martinfowler.com, Statsig’s offline evals and AI evals overview for tooling and workflows docs.statsig.com docs.statsig.com, plus community advice on custom RAG benchmarks and tougher eval prompts reddit.com reddit.com. Hope you find this useful!