---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 4e10e5d1a0d3782f6db32eb66690531d9fd20d3e2c46015f1f0a5c551c700984
  pageDirectory: concepts
  sources:
    - concepts-data-model-databricks-on-aws.md
    - evaluation-runs-in-mlflow-databricks-on-aws.md
  confidence: 1
  provenanceState: merged
  freshnessStatus: unverified
  aliases:
    - evaluation-run
    - evaluation
    - Evaluate your app
    - RAG evaluation
    - RAG quality evaluation
  citations:
    - file: evaluation-runs-in-mlflow-databricks-on-aws.md
    - file: concepts-data-model-databricks-on-aws.md
title: Evaluation Run
description: The results of testing an application version against an evaluation dataset using scorers, containing the generated traces, their assessments, and aggregated quality metrics.
tags:
  - mlflow
  - evaluation
  - testing
timestamp: "2026-06-19T14:21:50.427Z"
---

# Evaluation Run

An **Evaluation Run** (also called an **evaluation run**) is an [MLflow Run](/concepts/mlflow-run.md) that organizes and stores the results of evaluating a GenAI application. It is the core output of the [Evaluation Harness](/concepts/evaluation-harness.md) and serves as the primary unit for tracking, comparing, and analyzing the quality of an application version across a curated set of test cases. ^[evaluation-runs-in-mlflow-databricks-on-aws.md] ^[concepts-data-model-databricks-on-aws.md]

## Overview

Evaluation runs provide a structured container for all data generated during a systematic quality evaluation of an application. Each evaluation run captures:

- **Traces**: One trace for each input in the [Evaluation Dataset](/concepts/evaluation-dataset.md).
- **Feedback**: Quality assessments from scorers attached to each trace.
- **Metrics**: Aggregate statistics computed across all evaluated examples.
- **Metadata**: Information about the evaluation configuration, such as the dataset name, model version, and which scorers were applied. ^[evaluation-runs-in-mlflow-databricks-on-aws.md]

Evaluation runs are used to determine if application changes improved or regressed quality, compare versions of an application side-by-side, and track quality evaluations over time. ^[concepts-data-model-databricks-on-aws.md]

## Creating Evaluation Runs

An evaluation run is automatically created when you call `mlflow.genai.evaluate()`. The function takes an evaluation dataset, a prediction function (your application), and a set of [[scorers]] as input, runs your application for every record in the evaluation dataset, runs each scorer on the resulting traces, and stores the results in a new evaluation run. ^[evaluation-runs-in-mlflow-databricks-on-aws.md] ^[concepts-data-model-databricks-on-aws.md]

```python
import mlflow

# This creates an evaluation run
results = mlflow.genai.evaluate(
    data=test_dataset,
    predict_fn=my_app,
    scorers=[correctness_scorer, safety_scorer],
    experiment_name="my_app_evaluations"
)

# Access the run ID
print(f"Evaluation run ID: {results.run_id}")
```

^[evaluation-runs-in-mlflow-databricks-on-aws.md]

## Evaluation Run Structure

```
Evaluation Run
├── Run Info
│   ├── run_id: unique identifier
│   ├── experiment_id: which experiment it belongs to
│   ├── start_time: when evaluation began
│   └── status: success/failed
├── Traces (one per dataset row)
│   ├── Trace 1
│   │   ├── inputs: {"question": "What is MLflow?"}
│   │   ├── outputs: {"response": "MLflow is..."}
│   │   └── feedbacks: [correctness: 0.8, relevance: 1.0]
│   ├── Trace 2
│   └── ...
├── Aggregate Metrics
│   ├── correctness_mean: 0.85
│   ├── relevance_mean: 0.92
│   └── safety_pass_rate: 1.0
└── Parameters
    ├── model_version: "v2.1"
    ├── dataset_name: "qa_test_v1"
    └── scorers: ["correctness", "relevance", "safety"]
```

^[evaluation-runs-in-mlflow-databricks-on-aws.md]

## Relationship to Other Entities

Evaluation runs are connected to several other entities in the [MLflow GenAI Data Model](/concepts/mlflow-genai-data-model.md):

- **[Traces](/concepts/traces.md)**: Each evaluation run contains the traces generated by running the application against the evaluation dataset. These traces have [Assessments](/concepts/assessments.md) attached containing the scoring results.
- **[[Scorers]]**: The set of scorers used in an evaluation run defines what quality dimensions are measured.
- **[Evaluation Datasets](/concepts/evaluation-datasets.md)**: The dataset used as input provides the test cases that the run evaluates against.
- **[Logged Models](/concepts/logged-models.md)**: Evaluation runs can be linked to specific application version snapshots to track quality over time.
- **[Prompts](/concepts/prompt-versioning.md)**: Evaluation runs can be linked to version-controlled prompt templates to track prompt quality.

^[evaluation-runs-in-mlflow-databricks-on-aws.md] ^[concepts-data-model-databricks-on-aws.md]

## Viewing Evaluation Runs

Evaluation runs can be viewed in the [MLflow Experiment UI](/concepts/mlflow-experiment.md), which provides visual access to evaluation results, aggregate metrics, and per-trace feedback. This allows developers to:

- Review the overall quality scores for an application version.
- Drill down into specific traces to understand failures.
- Compare evaluation runs across different application versions or prompt configurations. ^[concepts-data-model-databricks-on-aws.md]

## Related Concepts

- [Evaluation Harness](/concepts/evaluation-harness.md) – The system that orchestrates the evaluation process
- [[Scorers]] – Functions that evaluate trace quality and produce feedback
- [Evaluation Datasets](/concepts/evaluation-datasets.md) – Curated collections of test cases used as input
- [Traces](/concepts/traces.md) – Execution logs captured during evaluation
- [Assessments](/concepts/assessments.md) – Quality measurements attached to traces
- [MLflow GenAI Data Model](/concepts/mlflow-genai-data-model.md) – The complete data model for GenAI applications
- [MLflow Experiment UI](/concepts/mlflow-experiment.md) – User interface for viewing evaluation results
- [Production Monitoring for GenAI](/concepts/production-quality-monitoring-mlflow-genai.md) – Continuous evaluation of production traces

## Sources

- evaluation-runs-in-mlflow-databricks-on-aws.md
- concepts-data-model-databricks-on-aws.md

# Citations

1. [evaluation-runs-in-mlflow-databricks-on-aws.md](/references/evaluation-runs-in-mlflow-databricks-on-aws-05902839.md)
2. [concepts-data-model-databricks-on-aws.md](/references/concepts-data-model-databricks-on-aws-1534caf0.md)
