> For the complete documentation index, see [llms.txt](https://karini-ai.gitbook.io/karini-ai-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://karini-ai.gitbook.io/karini-ai-documentation/prompt-management/prompt-and-agent-evaluation/analyzing-results.md).
# Analyzing results
After your evaluation completes, Karini AI provides comprehensive analytics to review performance and identifying improvement opportunities.
### Results Dashboard
#### **Overall Performance Summary**
The dashboard provides a high-level view of your evaluation:
* **Status**: Current state of evaluation (completed, processing, failed)
* **Evaluation Date**: When the evaluation was run
* **Dataset Size**: Number of test cases evaluated
* **Prompt Version**: Which version was evaluated
* **Evaluation Strategy**: Which metrics were used (default, custom, or both)
#### **Metric Performance Cards**
Each metric is shown as a **performance card**, providing a quick snapshot of behavior across the dataset:
* **Average Score** – Mean score for the metric
* **Score Distribution** – Visual representation of the spread of scores (e.g., histogram)
* **Confidence Level** – Average evaluator confidence for that metric
These cards help you quickly identify which dimensions (e.g., relevancy, faithfulness, tool accuracy) are strong or weak.
**Statistical Analysis**
Understand performance variability:
* **Mean**: Average score across dataset
* **Median**: Middle value, showing central tendency
* **Standard Deviation**: Consistency measure (lower = more consistent)
* **Min/Max Values**: Range showing best and worst performance
* **Percentiles**: Distribution of scores across the dataset