How is this different from ordinary test data management?

Traditional test data often checks deterministic behaviour: given this input, expect this output. LLM evaluation data also has to capture ambiguous tasks, graded rubrics, prompt versions, judge configuration, and acceptable variation. The registry is not just storing examples; it is preserving the conditions under which a subjective or probabilistic result was judged.

Do all LLM evaluations need expected outputs?

No. Expected outputs are useful for extraction, classification, SQL generation, and other tasks with clear targets. For open-ended tasks, the better artefact is often a rubric, a list of required properties, or examples of unacceptable behaviour. The honest answer is that it depends on whether correctness can be specified as a value, a constraint, or a judgement.

What are leakage controls in an evaluation registry?

Leakage controls record and limit where evaluation examples have been exposed. An eval item loses value if it appears in training data, few-shot prompts, documentation, or a retrieval corpus used by the system under test. Controls usually include provenance, sensitivity labels, split membership, access rules, and review before examples move between training and held-out evaluation.

LLM Evaluation Data Management

LLM evaluation data management is the practice of versioning and governing the prompts, inputs, reference answers, rubrics, metadata, and leakage controls used to test an LLM application. It makes evaluation reproducible, so changes to a model, prompt, retriever, or tool chain can be compared against stable tasks rather than remembered impressions.

LLM systems are hard to evaluate because the thing under test is often not just a model. A release may change the system prompt, retrieval index, tool schema, judge prompt, safety policy, or post-processing code. If the evaluation examples live in spreadsheets, notebooks, or chat logs, engineers cannot tell whether a score moved because behaviour improved, the test changed, or the grading standard drifted.

A registry makes the evaluation set a first-class artefact. Each task records what capability is being tested, the input examples, the prompt or instruction version, expected outputs where they exist, and the scoring method. Some checks are deterministic, such as JSON shape or an exact field value. Others use rubrics that describe acceptable, strong, and failing answers for humans or model-based judges to apply consistently.

The main trade-off is operational discipline. Someone has to review new cases, maintain ownership, update rubrics when product behaviour changes, and keep task versions interpretable. Judge-based evaluation adds another dependency: the judge prompt and judge model are themselves evaluation components that can drift. A common misunderstanding is that a larger eval set automatically means better evaluation. Coverage, independence, and clear failure definitions matter more than volume.

Engineers meet LLM evaluation data management in CI checks, release gates, prompt experiments, RAG regression suites, fine-tuning workflows, and safety reviews. It usually appears as a dataset registry or experiment-tracking workflow connected to model runs. Good setups also track exposure history, separating training, validation, and held-out evaluation data so prized test cases do not leak into prompts, tuning data, documentation, or retrieval corpora.

Go deeper

Everything DataLLM Evaluation Data ManagementCreate an evaluation dataset registry with task definitions, versioned prompts, expected outputs, rubrics, and leakage controls.Open the lesson

Common questions

How is this different from ordinary test data management?: Traditional test data often checks deterministic behaviour: given this input, expect this output. LLM evaluation data also has to capture ambiguous tasks, graded rubrics, prompt versions, judge configuration, and acceptable variation. The registry is not just storing examples; it is preserving the conditions under which a subjective or probabilistic result was judged.
Do all LLM evaluations need expected outputs?: No. Expected outputs are useful for extraction, classification, SQL generation, and other tasks with clear targets. For open-ended tasks, the better artefact is often a rubric, a list of required properties, or examples of unacceptable behaviour. The honest answer is that it depends on whether correctness can be specified as a value, a constraint, or a judgement.
What are leakage controls in an evaluation registry?: Leakage controls record and limit where evaluation examples have been exposed. An eval item loses value if it appears in training data, few-shot prompts, documentation, or a retrieval corpus used by the system under test. Controls usually include provenance, sensitivity labels, split membership, access rules, and review before examples move between training and held-out evaluation.

Every term