---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: be9239437ed0901dca8237f26d56b0d3eb04ea0e309fa4bfe1ff9fefa5db6d5f
  pageDirectory: concepts
  sources:
    - evaluation-runs-in-mlflow-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - evaluation-traces
    - Evaluating Traces
    - Evaluation Metrics
    - Evaluation metrics
    - Evaluation task
    - Analyzing traces
  citations:
    - file: evaluation-runs-in-mlflow-databricks-on-aws.md
title: Evaluation Traces
description: Per-input records within an evaluation run, each containing inputs, outputs, and associated feedback for one example in the evaluation dataset.
tags:
  - mlflow
  - traces
  - evaluation
timestamp: "2026-06-19T18:43:47.870Z"
---

# Evaluation Traces

**Evaluation Traces** are a core component of [MLflow](/concepts/mlflow.md) [Evaluation Runs](/concepts/evaluation-runs.md) that capture the input, output, and feedback for each individual example in a GenAI evaluation dataset. Each trace represents a single invocation of the application being evaluated and serves as the primary unit of record for understanding model behavior at the per-example level.

## Structure

An evaluation run contains one trace for each row in the evaluation dataset. Every trace includes the specific **inputs** provided to the model and the **outputs** generated by the model in response. Additionally, each trace stores the **feedback** from [[scorers]] that were attached during evaluation, providing quality assessments at the individual example level. ^[evaluation-runs-in-mlflow-databricks-on-aws.md]

The hierarchical organization of an evaluation run places traces as direct children of the run itself, co-located with other components such as aggregate metrics, parameters, and run metadata. This structure allows users to inspect the detailed results of any single evaluation example alongside the broader summary statistics. ^[evaluation-runs-in-mlflow-databricks-on-aws.md]

## Example

A typical trace for a question-answering evaluation might contain:

```
Trace ├── inputs: {"question": "What is MLflow?"}
     ├── outputs: {"response": "MLflow is..."}
     └── feedbacks: [correctness: 0.8, relevance: 1.0]
```

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

In this example, the trace stores the question passed as input, the model's generated response, and the quality scores from two different [[scorers]] (correctness and relevance). Multiple traces are collected in the evaluation run to form a complete evaluation dataset. ^[evaluation-runs-in-mlflow-databricks-on-aws.md]

## Relationship to Evaluation Runs

While [Evaluation Runs](/concepts/evaluation-runs.md) organize the entire evaluation workflow and aggregate results into summary statistics, traces provide the granular, per-example view. Traces are automatically created when `mlflow.genai.evaluate()` is called, with one trace created per dataset row. ^[evaluation-runs-in-mlflow-databricks-on-aws.md]

The combination of traces and [Aggregate Metrics](/concepts/aggregate-metrics.md) enables both detailed debugging of individual failures and high-level assessment of model performance across the entire evaluation dataset. Parameters such as `model_version` and `dataset_name` apply to all traces within the same evaluation run. ^[evaluation-runs-in-mlflow-databricks-on-aws.md]

## Related Concepts

- [Evaluation Runs](/concepts/evaluation-runs.md) — The parent container that stores traces, metrics, and metadata
- [[Scorers]] — Functions that produce the feedback stored in each trace
- [Aggregate Metrics](/concepts/aggregate-metrics.md) — Summary statistics computed across all traces in a run
- [Inference log analysis](/concepts/inferencelog-analysis.md) — Related concept for monitoring model predictions over time

## Sources

- evaluation-runs-in-mlflow-databricks-on-aws.md

# Citations

1. [evaluation-runs-in-mlflow-databricks-on-aws.md](/references/evaluation-runs-in-mlflow-databricks-on-aws-05902839.md)
