Principle:Mlflow Mlflow Trace Assessment
| Knowledge Sources | |
|---|---|
| Domains | ML_Ops, LLM_Observability |
| Last Updated | 2026-02-13 20:00 GMT |
Overview
Attaching human or automated quality evaluations, ground truth labels, and structured feedback to captured execution traces.
Description
Trace Assessment is the principle of enriching execution traces with evaluative metadata after they have been collected. While traces capture what happened during execution (inputs, outputs, timing, errors), assessments capture judgments about the quality and correctness of those executions. This separation of observation from evaluation is fundamental to building effective feedback loops in AI systems.
Assessments fall into two distinct categories. Expectations represent ground truth labels -- the correct or desired output for a given input. These are typically provided by human annotators or derived from curated datasets, and they serve as the reference standard against which system outputs are compared. Feedback represents qualitative or quantitative evaluations of the actual output, such as relevance scores, faithfulness ratings, or binary correctness labels. Feedback can originate from human reviewers, heuristic scoring functions, or LLM-as-a-Judge evaluators.
Each assessment is associated with a specific trace (and optionally a specific span within that trace) and carries provenance metadata through an assessment source that identifies whether the evaluation came from a human, an LLM judge, or automated code. This provenance tracking is essential for understanding the reliability and potential biases of different evaluation signals. Assessments also support error reporting, allowing the system to record when an evaluation attempt failed (for example, due to rate limiting on a judge LLM) without losing the fact that the evaluation was attempted.
Usage
Use trace assessments to build evaluation datasets from production traces, implement human-in-the-loop review workflows, run automated quality scoring over trace collections, and track ground truth labels for regression testing. Assessments are the bridge between raw observability data and actionable quality metrics.
Theoretical Basis
The assessment model draws on evaluation methodology from information retrieval and natural language processing, where system outputs are judged against reference standards using inter-annotator agreement frameworks. The separation of expectations from feedback mirrors the distinction between ground truth and predicted labels in supervised learning evaluation. The source provenance model implements a simplified W3C PROV-style lineage, ensuring that every quality judgment can be traced back to its origin. This is critical for the emerging practice of "LLM-as-a-Judge" evaluation, where understanding which model produced which scores enables meta-evaluation of the judges themselves.