SAG / ARCHITECTURE NOTE
RAG Provenance and Citation Tracking: Tracing Answers Back to Their Sources
Explains the definition and need for RAG provenance, how it works, and the criteria for applying it in SAG architecture, with practical checklists and support from research and official documentation.
Definition in one sentence
RAG provenance is a structure that tracks the origin of results by linking answer statements, retrieved documents, source locations, and execution versions.
Key answer: A list of URLs alone makes it difficult to tell which evidence supports which statement. When a document changes, the same URL may also contain different content.
Why is this technology needed?
A list of URLs alone makes it difficult to tell which evidence supports which statement. When a document changes, the same URL may also contain different content.
How it works
Store document IDs and versions, chunk locations, retrieval scores, and whether each item was used, and link answer claims to citations. Verify whether the source has changed using its hash.
When designing a system, accuracy is not the only consideration. Latency, cost, data boundaries, update frequency, and behavior in the event of failure must also be defined to produce reproducible results in production. For values that automation cannot determine with confidence, it is safer to leave them in a “not measured” or “needs review” state rather than changing them to 0 or treating them as successful.
Connection to SAG technology
SAG has a structure that records input, collection, and normalization hashes, as well as rule, model, prompt, and schema versions, in report provenance. This record increases the auditability of evidence-based answers.
Practical checklist
- Check that citations support the actual statements
- Store document versions and collection timestamps
- Establish a policy for handling deleted or changed sources
- Distinguish the states of failures, empty results, and permission errors from success
- Revalidate before and after changes under the same conditions
Research and official documentation
Reference documents support the principles and recommendations. They do not guarantee search visibility, AI mentions, rankings, or revenue; the actual effects of implementation should be verified through observations of service data under the same conditions.
How to continue reading about this technology
Distinguish monthly samples, the denominator for citation rate, competitive baselines, and Goal attainment rates.
SAG / KNOWLEDGE LINKS