Open Data Infrastructure
AI-Ready Context Evaluation Datasets
How context evaluation datasets preserve source lineage, policy state, freshness, retrieval paths, and answer boundaries.
An evaluation dataset that stores only prompts is not evaluating context. It is evaluating memory of a conversation.
Context needs its own test data
AI-ready context is data plus the metadata, policy, lineage, freshness, and meaning an AI system needs to use it responsibly. That means evaluation datasets need more than input prompts and expected answers.
A serious context evaluation dataset should preserve the source documents or tables, retrieval path, ranking signals, policy state, freshness state, expected answer boundary, and the reason a source is allowed or denied.
Evaluation should test boundaries
The system should be tested on correct retrieval, stale retrieval, denied retrieval, ambiguous ownership, missing lineage, and overbroad answers. Those cases tell you whether the context layer supports production use, not just whether the model can sound plausible.
OpenAI eval tooling and broader AI risk frameworks make evaluation a first-class engineering practice. ODI adds the data-infrastructure side: which context was available, why it was available, and whether it should have been available.
Core idea: context evaluation is about evidence boundaries, not only answer quality.
The ODI pattern keeps context reproducible
Open Data Infrastructure gives evaluation datasets a stable way to reference source data, lineage, policies, freshness, owners, and data contracts. That lets teams rerun tests when the data changes, not only when the model changes.
For adjacent context, read AI-ready context quality tests, AI-ready context lineage fingerprints, and context graphs for retrieval governance.
What breaks first
- The eval stores the expected answer but not the source state that made the answer valid.
- Retrieval improves benchmark scores by using context the user should not receive.
- Freshness changes, but the eval still treats old answers as correct.
- Denied retrieval paths are never tested, so policy regressions look like recall improvements.
Questions to ask
Ask whether the dataset can reproduce the context window, source lineage, policy decision, and freshness state for each test case. Ask whether a wrong answer can be traced to model behavior, retrieval behavior, or data infrastructure behavior.
Sources to start with
These primary sources anchor the technical claims in this guide.
- OpenAI evals guide
- NIST AI Risk Management Framework
- W3C PROV overview
- OpenLineage object model documentation
If the context cannot be replayed, the evaluation cannot explain what it measured.