> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://humanloop.com/docs/v5/guides/evals/run-human-evaluation/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://humanloop.com/_mcp/server.

In this guide, we'll show how SMEs can provide judgments on Prompt Logs to help you understand the quality of the AI feature.
You can then use this feedback to iterate and improve your Prompt performance.

### Prerequisites

* You have set up a Human Evaluator appropriate for your use-case. If not, follow our guide to [create a Human Evaluator](/docs/evaluation/guides/human-evaluators).
* You have a Dataset with test data to evaluate model outputs against. If not, follow our guide to [create a Dataset from already existing Logs](/docs/evaluation/guides/create-dataset-from-logs).

## Provide judgments on Logs

In this guide, we assume you have already created a Prompt and a Dataset for an evaluation. Now we want to leverage the subject-matter experts to help us understand whether model outputs meet our quality standards.

### Create a new Evaluation

Navigate to the Prompt you want to evaluate and click on the **Evaluation** tab at the top of the page. Click on **Evaluate** to create a new Evaluation.

![](/docs/_fern-img/2dc314a9f85c9875407b3c849708c0cd921c2137f60c0483be7d5895757a176e.webp)

### Create a new Run

To evaluate a version of your Prompt, click on the **+Run** button, then select the version of the Prompt you want to evaluate and the Dataset you want to use.
Click on **+Evaluator** to add a Human Evaluator to the Evaluation.

> **Note**
>
> You can find example Human Evaluators in the **Example Evaluators** folder.

![](/docs/_fern-img/b370af190b502385b71217173b33392bd678af11093b0c9e81dec7f73e89a622.webp)

Click **Save** to create a new Run. Humanloop will start generating Logs for the Evaluation.

### Apply judgments to generated Logs

When Logs are generated, navigate to the **Review** tab. Turn on **Focus mode** and start providing judgments on the generated Logs.

![](/docs/_fern-img/51a46e72159e800bdb20b00a56aed42a85fb541e015646c2b18170e8eae53ae8.webp)

When the last judgment is provided, the Run is marked as complete.

![](/docs/_fern-img/028967200b4cc2171504055104f4bb9786fab38d5c39938b2f73b206f41d401f.webp)

### Review judgments stats

You can see the overall performance across all Evaluators in the **Stats** tab.

![](/docs/_fern-img/91c89a52b92f575c9fb3c85d3ececa58a1661d7620d5398fc9636cc85fef7054.webp)

## Improve the Prompt

Explore the Logs that the SME flagged in the Review tab.

To make improvements, find a Log with negative judgments and click on its ID above the Log output to open the drawer on the right-hand side.
In the drawer, click on the **Editor ->** button to load the Prompt Editor. Now, modify the instructions and save a new version.

![Run Evals with Dataset on Humanloop.](/docs/_fern-img/9e9b5bb5743a112993edb16783cdad50d3890e29419c4ad25f35661d6bd72272.webp)

Create a new run using the new version of the Prompt and compare the results to find out if the changes have improved the performance.

## Next steps

We've successfully collected judgments from the SMEs to understand the quality of our AI product.
Explore next:

* If your team has multiple internal SMEs, learn how to [effectively manage evaluation involving multiple SMEs](/docs/evaluation/guides/manage-multiple-reviewers).
* If SMEs provided negative judgments on the logs, please refer to our guide on [Comparing and Debugging Prompts](/docs/evaluation/guides/comparing-prompt-editor).

> Collect judgments from subject-matter experts (SMEs) to better understand the quality of your AI product.