RAG and Eval Dataset Governance
How to govern RAG knowledge data and evaluation datasets with repository workflows and evidence gates.
Definition
RAG and eval dataset governance is the practice of versioning, reviewing, gating, releasing, and rolling back the data assets that ground or evaluate AI systems.
Problem
AI behavior changes when documents, chunks, prompts, labels, or examples change. If those assets are not versioned and approved, teams cannot explain regressions or reproduce results.
Styxis perspective
Styxis builds software for sensitive data validation and AI governance, guided by Oath, Boundary, and Crossing: it binds responsibility to action, distinguishes operating states, and moves verified change into execution.
Product connection
Truthound Depot keeps imported RAG and eval datasets as encrypted Data Versions, or validates selected Cloud Source scopes, then connects Draft, Compare, Validation, Review Request, Evidence, Published Version, and Restore.
FAQ
Why govern RAG data separately?
RAG knowledge changes model answers without retraining the model, so document changes need review, evidence, and release control.
What belongs in eval dataset governance?
Prompts, expected answers, labels, examples, scoring rubrics, negative cases, and production incident cases should be versioned together.
How does Styxis make this concrete?
Styxis treats RAG and eval data as release assets, not loose documents, and connects evidence to each promotion decision.