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 turns events in automated operational environments, from data changes to industrial machine signals, into traceable, verifiable proof.
Product connection
Truthound Depot reviews RAG knowledge data and eval dataset changes as metadata-only Depot workflows with branch, compare, quality gate, approval, release, and rollback evidence.
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.