> ## Documentation Index
> Fetch the complete documentation index at: https://snowglobe.so/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Creating a Custom LLM Metric

> Learn how to create a custom LLM metric in Snowglobe to evaluate AI agents against your unique criteria.

# Creating a Custom LLM Metric

**Custom LLM Metrics** allow you to evaluate your AI agent’s performance against criteria you define. Instead of relying only on built-in metrics, you can write your own metric prompts so Snowglobe’s evaluation system measures what matters most to your use case.

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## Step 1. Start in the Metrics Dashboard

Go to [Snowglobe Metrics](https://snowglobe.so/app/metrics) and click **Add Custom Metric**.\
This will open the metric editor where you can configure and test your new metric.

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## Step 2. Name the Metric

Give your metric a **name**.\
This acts as an identifier, so keep it simple and descriptive (e.g., `factual_accuracy`, `tone_politeness`, `sales_closure_success`).

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## Step 3. Write a Description

Write a **short description** of what your metric should evaluate. This helps collaborators understand its purpose.

For example:

> *This metric evaluates whether the AI agent’s responses remain polite and professional, even when the user is frustrated or rude.*

The description is **human-readable only** and doesn’t affect how Snowglobe evaluates—it's just documentation.

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## Step 4. Generate and Edit the Metric Prompt

Click **Generate Prompt** to automatically create a starting point for your metric prompt.\
The generated prompt will include:

* A description of the criteria to judge
* Instructions for how the model should score the conversation

Now, **customize the criteria** in the prompt until it matches your use case.

⚠️ **Important:** Do not change the **input/output format**. Snowglobe depends on this structure to process the results reliably.

## Step 5. Choose your Metric Model

Select the **LLM** that Snowglobe should use to evaluate this metric. We recommend using bigger models like gpt-4o or gpt-5 if your criteria are sophisticated

## Step 6. Use your custom metric

Once you’re happy with your metric prompt, click **Save Metric**.\
Your new metric will now appear in the list of available metrics when you create a simulation.
We recommend that you review your custom metric’s performance after running a simulation to ensure it’s working as expected. You can always edit the metric later if needed.