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

# Analyze evaluation reports

> Review, compare, and act on detailed evaluation results to improve your prompts

After an evaluation completes, Adaline generates a detailed report with per-row results, evaluator scores, and comparison tools. Use these reports to identify failures, spot patterns, and drive prompt improvements.

## View evaluation results

Once your evaluation finishes, the results are displayed automatically:

![Evaluation results overview](https://mintcdn.com/adaline/Um_T8BffW4hfcoYD/images/evaluate/visualize-results.png?fit=max\&auto=format\&n=Um_T8BffW4hfcoYD\&q=85\&s=ad67cebf902271c0dae8db2724217327)

## Inspect individual test cases

Click on any row to inspect the details of a specific test case — including the model's full response, each evaluator's score, and the reason for pass or failure:

![Inspecting an individual test case](https://mintcdn.com/adaline/Um_T8BffW4hfcoYD/images/evaluate/inspecting-run.png?fit=max\&auto=format\&n=Um_T8BffW4hfcoYD\&q=85\&s=f1fc7b615370bef187304d45e93d0fe1)

## Filter results

Use the **Filter** option to narrow down the results and focus on what matters most:

![Filtering evaluation results](https://mintcdn.com/adaline/Um_T8BffW4hfcoYD/images/evaluate/filtering-results.png?fit=max\&auto=format\&n=Um_T8BffW4hfcoYD\&q=85\&s=56c21562626f0316d18674d6c87a34b9)

Common filters include:

| Filter | Use case |
| - | - |
| **Pass/Fail** | Focus on failing test cases to prioritize fixes. |
| **Evaluator type** | Isolate results from a specific evaluator (e.g., only LLM-as-a-Judge scores). |
| **Score range** | Find test cases with borderline scores that may need attention. |
| **Search** | Find specific patterns or keywords in test case data. |

## Compare across evaluation runs

When you run multiple evaluations, you can navigate between each run's results using the graph view. Hover over a point to see the summary:

![Navigating between evaluation runs](https://mintcdn.com/adaline/Um_T8BffW4hfcoYD/images/evaluate/move-among-evaluations.png?fit=max\&auto=format\&n=Um_T8BffW4hfcoYD\&q=85\&s=2a4d5500c06a17a84d6cfff779e7cdad)

Click on a point in the graph to view that run's detailed results. The **Go to latest** button redirects to the most recent run. You can view and compare any of the last 20 evaluation runs.

This is valuable for:

* **Tracking progress** — See how scores improve as you refine your prompt.
* **A/B testing** — Compare results between different prompt versions or model configurations.
* **Regression detection** — Spot cases where a prompt change caused previously passing test cases to fail.

## Act on insights

Use report data to drive systematic prompt improvements:

1. **Prioritize failures** — Focus on test cases that fail consistently across multiple runs.
2. **Open in Playground** — Click on any failing test case to open it in the [Playground](https://www.adaline.ai/docs/iterate/run-prompts-in-playground.md) for interactive debugging.
3. **Identify patterns** — Look for common themes in failures (certain input types, specific evaluator criteria, edge cases).
4. **Refine and re-evaluate** — Update your prompt in the [Editor](https://www.adaline.ai/docs/iterate/overview.md), then run a new evaluation to measure the impact.
5. **Compare runs** — Use the run comparison graph to verify that your changes improved overall scores without introducing regressions.

> [!TIP]
>
> When you identify a common failure pattern, add more test cases to your dataset that target that specific pattern. This builds a robust test suite that catches regressions as your prompt evolves.

## Next steps

> [card] [Evaluate Prompts](https://www.adaline.ai/docs/evaluate/evaluate-prompts.md)
>
> Run another evaluation with updated prompts.

> [card] [Deploy Your Prompt](https://www.adaline.ai/docs/deploy/overview)
>
> Deploy your validated prompt to production.


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