12.08 · Walkthrough
LLM Evaluation Data Management
Create an evaluation dataset registry with task definitions, versioned prompts, expected outputs, rubrics, and leakage controls.
An LLM evaluation dataset registry is the controlled record of tasks, prompts, references, rubrics, metadata and leakage status used to compare model behaviour across changes. It gives engineers repeatable evaluations for prompt edits, model swaps, retrieval changes and tool integrations, while protecting held-out examples from contaminating training or context.
What this lesson answers
- how to structure an LLM evaluation dataset registry
- how to version prompts and rubrics for evals
- how to prevent leakage in LLM evaluation data
Notes
An evaluation dataset registry is the system of record for everything needed to judge whether an LLM-based application is improving, regressing, or behaving safely. Instead of keeping test prompts in ad hoc spreadsheets or notebooks, the registry stores task definitions, input examples, prompt versions, expected outputs, scoring rubrics, metadata, and ownership information in a structured, versioned way.
Common questions
- What belongs in an LLM evaluation dataset registry?
- A useful registry stores the task being tested, inputs, prompt or instruction versions, reference outputs where they exist, scoring rubrics, metadata, owners and change history. It should also record source, sensitivity and exposure status so engineers can tell whether a case is safe for held-out evaluation or has already been used elsewhere.
- Why not keep LLM eval prompts in a spreadsheet?
- A spreadsheet can work briefly, but it usually loses traceability once prompts, models, retrieval settings and rubrics start changing independently. A registry makes evaluation inputs and scoring rules versioned artefacts, so a regression result can be tied to the exact data, prompt and rubric used at the time.
- How do leakage controls affect LLM evaluation quality?
- Leakage makes evaluation results less trustworthy because the model may have already seen the examples through training, tuning, documentation, few-shot prompts or retrieval content. Labelling exposure history and separating training, validation and held-out sets helps preserve eval cases as genuine tests rather than memorisation checks.
Short definition: what is LLM Evaluation Data Management?