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What a source-ID check can—and cannot—prove
Source-ID validation has two separate parts: reference integrity and evidence quality. A reference-integrity check asks whether the exact ID appears in the answer’s retrieval set and resolves to the expected document or chunk. An evidence-quality check asks whether that passage supports the claim, for the right subject and scope.
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Keep those results distinct. A citation can pass the ID check and still point to irrelevant material, support only part of a sentence, or describe a different entity, event, or version. Google Cloud defines perfect grounding as requiring every claim in an answer candidate to be supported by one or more supplied facts; partial entailment does not meet that standard. Google Cloud’s grounding documentation describes this criterion and shows how claims can be connected to cited chunks.
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A practical validation workflow
- Freeze the retrieval set. For each generated answer, retain the exact document and chunk IDs supplied to the model. Keep enough metadata to resolve those IDs later; otherwise, a citation may be impossible to reproduce or inspect.
- Check every emitted ID against an allowlist. Parse the answer’s citation markers and compare each exact ID with the frozen retrieval set. Flag IDs that are absent, malformed, or duplicated in a way your citation format does not allow. Resolve valid IDs through an application-owned registry; do not trust a model-generated title or URL as proof of identity.
- Resolve the reference to its source. Confirm that the ID maps to the intended document and passage. Retain useful provenance such as the document title or filename, chunk identifier, and URL where applicable. OWASP recommends attributing retrieved documents, used chunks, and their provenance; Microsoft notes that index fields such as titles, URLs, and filenames can improve citation quality. See the OWASP RAG Security Cheat Sheet and Microsoft Foundry’s RAG documentation.
- Break the answer into reviewable claims. Record which chunk ID or IDs support each material claim, rather than treating a paragraph as one indivisible citation unit. Google Cloud’s grounding response can expose cited chunks and connect claims to citations, providing a model for this claim-to-evidence mapping.
- Read the cited passage against the whole claim. Decide whether it entails the full assertion, contradicts it, or leaves it unsupported. Watch for partial support: a passage may establish a date but not a causal claim, or support a statement about one version without supporting a broader statement about all versions.
- Check attribution and coverage. Verify that the evidence concerns the same entity, event, version, and scope named in the claim. Also identify material claims with no supporting citation; a set of valid IDs does not make uncited assertions grounded.
- Escalate uncertainty and high-impact claims. Use automated scores to prioritize review, not to replace it. Where the consequences of an error are substantial, have a qualified reviewer examine the source and claim.
Use a four-part review rubric
| Check | Question | What a failure means |
|---|---|---|
| ID membership | Is this exact source or chunk ID in the retrieval set for this answer? | The citation points outside the allowed set or cannot be validated. |
| Resolution and provenance | Does the ID map to the expected document and passage, with enough metadata to identify it? | The reference cannot be reliably traced or reproduced. |
| Claim support and attribution | Does the passage support the complete claim about the entity, event, version, and scope stated? | The evidence is irrelevant, partial, contradictory, or misattributed. |
| Coverage | Does every material claim have supporting evidence? | One or more assertions remain uncited or unsupported. |
These checks catch different errors. Passing membership and resolution is necessary for a trustworthy citation trail, but only the support and coverage checks address whether the answer is actually grounded.
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Where automated grounding checks fit
A grounding service can help compare answer claims with supplied facts and expose support information. Google Cloud documents a support score and citation-threshold behavior in its check-grounding documentation. Treat such outputs as triage signals: they can help route uncertain claims for review, but the documentation does not establish one universally correct threshold for every domain or application.
Set any cutoff according to the application’s subject matter and the cost of unsupported claims. A score is not a substitute for confirming ID membership, resolving provenance, checking attribution, and reading the evidence. The documented 0-to-1 scale describes a score output, not a measured accuracy rate.
Include retrieval security in the same process
Retrieved passages are evidence to assess, not instructions to obey. OWASP and Microsoft both flag prompt injection in retrieved content as a RAG risk and recommend controls across the pipeline. Treat retrieved text as untrusted input, validate model output before using it, and keep application logic—not generated citations—in control of downstream actions. The OWASP guidance and Microsoft’s RAG guidance describe these broader safeguards.
What to retain for an auditable answer
- The exact retrieval set and chunk IDs used to generate the answer.
- The mapping from each emitted citation ID to its document and passage.
- Useful source metadata, such as title or filename and URL where available.
- The claim-to-chunk mapping, including claims that lack support.
- The reviewer’s support and attribution decisions, plus any automated signal used to prioritize the review.
Keeping these records together makes it possible to distinguish an invalid reference from a valid reference that does not support its claim, and to reproduce the review later.
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