Evaluation Runs

Evaluation runs are MLflow runs that organize and store the results of evaluating your GenAI app.

What are evaluation runs?

An evaluation run is a special type of MLflow run that contains:

• Traces: One trace for each input in your evaluation dataset
• Feedback: Quality assessments from scorers attached to each trace
• Metrics: Aggregate statistics across all evaluated examples
• Metadata: Information about the evaluation configuration

Think of it as a test report that captures everything about how your app performed on a specific dataset.

Structure of an evaluation run

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"]

Creating evaluation runs

Evaluation runs are created automatically when you call mlflow.genai.evaluate():

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}")

Next Steps

• Evaluate your app - Create your first evaluation run
• Build evaluation datasets - Prepare data for consistent evaluation runs
• Compare evaluation runs - Learn to analyze and compare run results
• Evaluation Datasets - See what data goes into evaluation runs