---
title: "How to Choose Production Traces for Evaluation"
description: "Build a representative, risk-aware sample without overfitting to easy traffic or noisy complaints."
canonical: "https://www.evalgate.com/guides/choose-production-traces-for-evals"
date-published: "2026-09-02"
last-updated: "2026-09-02"
category: "Production quality"
keywords: "production traces for evals, sample LLM traces, trace to evaluation dataset"
---

# How to Choose Production Traces for Evaluation

Build a representative, risk-aware sample without overfitting to easy traffic or noisy complaints.

**Question:** Which production traces should I turn into evaluation cases?

## Short answer

Use a layered sample: random traces for the base rate, stratified traces for important intents and user groups, triggered traces for risk and novelty, and confirmed incidents for regression coverage. Review labels and privacy before promotion.

## Key takeaways

- Random sampling estimates prevalence but misses rare costly failures.
- Triggered samples find risk but cannot estimate the base rate alone.
- Keep sampling, labeling, and promotion as separate decisions.

## Signs this is the problem

- The dataset consists only of thumbs-down conversations.
- High-volume easy intents crowd out rare critical work.
- Raw production text is promoted without redaction or consent review.

## Step-by-step approach

### 1. Establish a random baseline

Sample eligible traffic consistently so overall quality trends have a defensible denominator.

### 2. Stratify important slices

Reserve coverage for intents, languages, plans, tools, and risk tiers that random volume may miss.

### 3. Add triggered evidence

Capture policy events, novel clusters, overrides, escalations, long runs, high cost, and low confidence.

### 4. Review before promotion

Confirm the expected behavior, minimize sensitive data, and preserve source and selection reason.

## What to measure

| Metric | What it measures | How to use it |
| --- | --- | --- |
| Sampling coverage | Eligible intent and risk slices represented in reviewed traces. | Fill consequential gaps rather than maximizing raw count. |
| Selection bias | Difference between sampled and eligible traffic distributions. | Report it so triggered sets are not mistaken for prevalence. |
| Promotion yield | Reviewed traces that become useful cases or documented learning. | Tune triggers when they produce mostly noise. |

## Common mistakes

- Using incidents to estimate ordinary failure rate.
- Sampling by convenience from one customer or workflow.
- Discarding the reason each trace entered the sample.

## Practical checklist

- [ ] Establish a random baseline: Sample eligible traffic consistently so overall quality trends have a defensible denominator.
- [ ] Stratify important slices: Reserve coverage for intents, languages, plans, tools, and risk tiers that random volume may miss.
- [ ] Add triggered evidence: Capture policy events, novel clusters, overrides, escalations, long runs, high cost, and low confidence.
- [ ] Review before promotion: Confirm the expected behavior, minimize sensitive data, and preserve source and selection reason.

## Where EvalGate fits

EvalGate can preserve trace provenance and review state before selected interactions become durable dataset cases.

[Review regression data practices](https://www.evalgate.com/docs/concepts/regression-data) or [start with the EvalGate quickstart](https://www.evalgate.com/docs/quickstart).

## Frequently asked questions

### What should I do first?

Sample eligible traffic consistently so overall quality trends have a defensible denominator

### How should the result be measured?

Eligible intent and risk slices represented in reviewed traces. Fill consequential gaps rather than maximizing raw count.

### When is the change ready to ship?

Ship only after the protected cases pass, the primary metric clears its agreed boundary, and the team reviews the remaining failure modes instead of relying on one aggregate score.

## Related guides

- [https://www.evalgate.com/guides/monitor-ai-quality-production](https://www.evalgate.com/guides/monitor-ai-quality-production)
- [https://www.evalgate.com/guides/build-ai-evaluation-dataset](https://www.evalgate.com/guides/build-ai-evaluation-dataset)
- [https://www.evalgate.com/guides/turn-production-failures-into-evals](https://www.evalgate.com/guides/turn-production-failures-into-evals)