---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 039404e4b2e11cdd231c0c21a5c78931d01ac96c5c93b113efca82cc453483fe
  pageDirectory: concepts
  sources:
    - concepts-data-model-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - traces
    - Trace
    - trace
    - Analyze Traces
    - Trace concepts
    - Trace events
    - Trace list
    - Traces tab
    - Trace|traces
    - traces-mlflow-genai
    - T(G
    - Trace (MLflow GenAI)
    - Tracing (MLflow GenAI)
  citations:
    - file: concepts-data-model-databricks-on-aws.md
title: Traces
description: Execution logs capturing inputs, outputs, and every intermediate step (LLM calls, retrievals, tool use) of a GenAI application, used for debugging, monitoring, and creating evaluation datasets.
tags:
  - mlflow
  - traces
  - observability
  - genai
timestamp: "2026-06-19T17:49:44.345Z"
---

# Traces

**Traces** are the core unit of observability in the [MLflow](/concepts/mlflow.md) GenAI data model. A trace captures the complete execution of a GenAI application, recording inputs, outputs, and every intermediate step — including LLM calls, retrievals, and tool invocations. Traces serve as the foundation for debugging, quality evaluation, and production monitoring of generative AI applications. ^[concepts-data-model-databricks-on-aws.md]

## Role in the Data Model

Traces are the only required entity in the [MLflow GenAI](/concepts/mlflow-3-for-genai.md) data model. While all other aspects (evaluation datasets, labeling sessions, prompts, etc.) are optional, every MLflow experiment is expected to contain traces. Each trace is an execution log stored within an [Experiment](/concepts/mlflow-experiment.md) container, which groups all data related to a single GenAI application. ^[concepts-data-model-databricks-on-aws.md]

## Creation and Linking

Traces are created automatically for every execution of a GenAI application, both in development and in production. They may optionally be linked to the specific application versions (logged models or prompts) that generated them, allowing traceability between code changes and observed behavior. ^[concepts-data-model-databricks-on-aws.md]

## Content of a Trace

Each trace carries attached [Assessments](/concepts/assessments.md) that provide quality measurements. Assessments come in two forms:

- **Feedback** – judgments about the quality of the app's outputs, added by [[Scorers]], end users, or domain experts.
- **Expectations** – ground truth labels that define the correct output for a given input, typically provided by domain experts.

Although expectations are not required for measuring quality with MLflow, they serve as a "gold standard" when available. ^[concepts-data-model-databricks-on-aws.md]

## Uses of Traces

Traces are used for two primary purposes: ^[concepts-data-model-databricks-on-aws.md]

1. **Observation and debugging** – Developers inspect traces to understand application behavior, identify errors, and monitor performance metrics such as latency and cost.
2. **Creating evaluation datasets** – Production traces can be selected to build [Evaluation Datasets](/concepts/evaluation-datasets.md), which are then used in systematic quality evaluation against new versions of the application.

## Relationship to Scorers and Evaluation

[[Scorers]] analyze traces to produce feedback assessments. The same scorer can be used during development (via `mlflow.genai.evaluate()`) and in production (via scheduled scorers). Evaluation runs produced by `evaluate()` contain both the traces generated by running the app on an evaluation dataset and the assessments from each scorer. ^[concepts-data-model-databricks-on-aws.md]

## Related Concepts

- [Experiment](/concepts/mlflow-experiment.md) – The container that holds all traces for a single application.
- [Assessment](/concepts/assessments.md) – Quality measurements (feedback and expectations) attached to traces.
- [Evaluation Dataset](/concepts/evaluation-dataset.md) – A curated set of inputs (often derived from traces) used for systematic evaluation.
- [Evaluation Run](/concepts/evaluation-run.md) – The result of testing an app version against an evaluation dataset, including traces and aggregated metrics.
- [[Scorers|Scorer]] – A function that evaluates a trace's quality and creates feedback assessments.
- [Labeling Session](/concepts/labeling-session.md) – A queue of traces for human expert review.
- [Logged Model](/concepts/loggedmodel.md) – A snapshot of an application version that can be linked to traces.
- Prompt – A version-controlled template that can be linked to traces.
- [Production Monitoring](/concepts/production-monitoring.md) – The use of scheduled scorers to automatically evaluate production traces.

## Sources

- concepts-data-model-databricks-on-aws.md

# Citations

1. [concepts-data-model-databricks-on-aws.md](/references/concepts-data-model-databricks-on-aws-1534caf0.md)
