---
title: Preferred output examples
slug: agent-platform/docs/preferred-output-examples
docTags: 
createdAt: 2026-08-12T15:37:08.000Z
---

Preferred output examples are agent outputs you save as quality benchmarks for Output Evaluation. Agent Platform uses these examples to score future runs of the same agent against your defined standard.

In Optimizely Agent Platform, find preferred output examples on the **Output Evaluation** sub-tab of the **Quality** tab. They display in the **Output Evaluation Examples (Single-Turn)** section. For multi-turn agents, use the **Conversation Evaluation Samples (Multi-Turn)** section instead.

For details on adding examples and configuring the rest of Output Evaluation, see [Configure Output Evaluation](docId\:CPd7lrluz3EZsm-imjXhi).

## How preferred output examples work

Preferred output examples drive Agent Platform's automated quality scoring. When you add an example, Agent Platform stores both the input variables and the resulting output. Agent Platform compares future runs of the agent against your examples using an LLM-as-Judge model. An LLM-as-Judge model is a Large Language Model (LLM) trained to evaluate other model outputs against quality standards. Each run produces an Evaluation Score between 0% and 100%.

Agent Platform marks runs at or above your baseline evaluation score as **Passed**. Agent Platform marks runs below the baseline as **Failed**. Set the baseline between 75% and 95% in 5% increments.

Preferred output examples work alongside the following other Output Evaluation inputs:

- **Evaluation criteria** – Statements that define what a quality output looks like.
- **Conversation samples** – Reference conversations for multi-turn agents.
- **Default rubric** – Quality dimensions covering accuracy, completeness, format consistency, and usefulness.

## When to use preferred output examples

Preferred output examples work best when your agent produces consistent, structured outputs. Use them in the following scenarios:

- You have an exemplary output that future runs should match.
- The output format is consistent in structure, length, or style.
- You want quality scoring to focus on similarity to specific examples rather than abstract criteria.

Evaluation criteria work better than examples for agents with high output variation, such as creative writing or brainstorming.

## Add preferred output examples

Add up to five preferred output examples per agent. Use one of two methods to add them.

- **Manually** – Click **Add Example** in the **Output Evaluation Examples** section and provide the input variables and preferred output.
- **From execution logs** – Open the run in the **Logs** tab, click **More**, and select **Link as Output Eval**.

See [Configure Output Evaluation](docId\:CPd7lrluz3EZsm-imjXhi) for the full procedure.

## Related articles

- [Configure Output Evaluation](docId\:CPd7lrluz3EZsm-imjXhi)
- [Quality tab overview](docId\:RyEJ8-_Fwgaw2WxYQKt1g)
- [Evaluations in Agent Platform overview](docId\:N0yz_Z40VOTC2kNn6txpB)
- [Create a specialized agent](docId\:tZPJWDyE1GSB-epLsUh5S)

:::hint{type="info"}
**Note**

If you use Opti ID, administrators can turn off generative AI in the Opti ID Admin Center. See [Turn generative AI off across Optimizely applications](docId\:X5U0kKD2azV5cIdNqwGNj).
:::