> ## Documentation Index > Fetch the complete documentation index at: https://docs.coval.ai/llms.txt > Use this file to discover all available pages before exploring further. # Metric Chaining > Combine metrics into custom logic flows for more efficient and accurate evaluations Metric Chaining allows you to create conditional metric flows where a follow-up metric runs only when specific criteria are met by a trigger metric. This approach helps you avoid cramming multiple evaluation checks into a single metric while ensuring accuracy and efficiency in your evaluations. Instead of creating one complex metric that tries to handle multiple scenarios, you can break down your evaluation logic into separate, focused metrics that run conditionally based on the results of previous metrics. ## Benefits of Metric Chaining * **Improved Accuracy**: Each metric focuses on a specific aspect of the conversation * **Efficiency**: Only run metrics that are relevant to the specific conversation flow * **Clarity**: Separate concerns make metrics easier to understand and maintain * **Flexibility**: Create complex evaluation logic without overwhelming single metrics ## How Metric Chaining Works 1. **Trigger Metric**: The primary metric that runs first and determines whether additional metrics should execute 2. **Follow-up Metric**: The secondary metric that runs conditionally based on the trigger metric's result 3. **Criteria**: The condition that determines when the follow-up metric should run (e.g., "equal to 0", "greater than 0") ## TL;DR Walkthrough: