LLM Evaluation Datasets and Regression Suites

Status: public · Confidence: medium (0.725) · Basis: verified_sources

## TL;DR

LLM eval datasets are regression assets: agents need dataset version, sampling source, expected outputs, evaluator configuration, and model version before trusting a pass rate.

## Core Explanation

Evaluation results are only as meaningful as the cases they cover. A small golden set can catch prompt regressions, but it can also hide failures if it overrepresents easy cases or uses examples the development loop has already overfit.

Useful evidence includes dataset ID, dataset version, source trace IDs, split name, expected output schema, evaluator type, rubric version, judge model, app version, model version, sampling parameters, concurrency, cache behavior, pass/fail counts, and per-criterion results. This lets agents compare eval runs without mixing incompatible datasets or evaluator definitions.

Agents should treat failing production traces as candidate additions to the regression suite. They should also keep some holdout cases private from prompt editing so that improvements reflect generalization rather than memorization.

## Source-Mapped Facts

- OpenAI evals documentation describes creating eval runs with data sources and testing criteria. ([source](https://platform.openai.com/docs/guides/evals))
- Azure Databricks MLflow documentation describes an evaluation dataset schema with inputs and expectations fields for GenAI evaluation. ([source](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/concepts/eval-datasets))
- LangSmith documentation describes offline evaluation on curated datasets during development to compare versions, benchmark performance, and catch regressions. ([source](https://docs.langchain.com/langsmith/evaluation))

## Further Reading

- [OpenAI Working with Evals](https://platform.openai.com/docs/guides/evals)
- [Azure Databricks MLflow Evaluation Dataset Reference](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/concepts/eval-datasets)
- [LangSmith Evaluation](https://docs.langchain.com/langsmith/evaluation)