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

# How can I Collect And Add User Feedback To A Trace?

> Collecting user feedback is essential for understanding how well your AI application meets user expectations and identifying opportunities for improvement. Maxim's observability platform includes a built-in Feedback entity that allows you to capture structured user ratings and comments directly within your application traces, creating a direct link between user satisfaction and specific system behaviors.

## Implementing User Feedback

You can add feedback to traces using Maxim's SDK across multiple programming languages:

* **JavaScript/TypeScript**: `trace.feedback({ score: 5, comment: "Great job!" })`
* **Python**: `trace.feedback(score=5, comment="Great job!")`
* **Go and Java**: Similar methods available through respective SDKs

The feedback structure supports:

* Numerical scores (typically 1-5 or custom rating scales)
* Optional text comments for qualitative insights
* Association with specific traces for granular analysis

## Leveraging Feedback Data

Once feedback is collected, Maxim provides powerful analysis capabilities:

* View feedback directly in trace details alongside technical metrics like latency, token usage, and cost
* Filter and search traces based on user satisfaction scores to identify patterns
* Track average user feedback over time through the Overview tab's aggregated metrics
* Correlate user satisfaction with specific prompt versions, model choices, or system configurations
* Set up alerts when feedback scores drop below acceptable thresholds

This feedback loop enables data-driven improvements to your AI applications, helping you prioritize optimization efforts based on actual user experience rather than technical metrics alone.