Claim-Level Source Grounding

Claim-level source grounding is an AI output verification architecture in which every discrete factual assertion in a generated response is individually checked against an approved source corpus before delivery — not the response as a whole, but each claim within it. It matters because a retrieved document can be topically relevant without supporting every sentence a model generates from it. Systems that skip this granularity allow hallucinated assertions to pass through on the credibility of surrounding supported claims. The practical takeaway: topic-level retrieval is not the same as claim-level verification.

Standard RAG retrieves documents that are topically relevant to a query; claim-level grounding requires each specific assertion in the generated output to resolve to a specific passage in an approved source. A document can be retrieved, present in context, and still fail to support a particular sentence the model generates from it. Systems that conflate retrieval with verification will pass hallucinated claims that are adjacent to, but not contained in, retrieved documents. The takeaway: retrieval is a necessary but not sufficient condition for grounded output.

Yes — this is the function of selective refusal, the third stage in claim-level source grounding. Once a response is decomposed into individual assertions and each is checked for attribution, the system can suppress only the ungrounded assertion and deliver the remainder, or trigger a full refusal depending on configured policy. In a lower-stakes research context, grounded claims may be delivered with the ungrounded portion withheld and flagged. The takeaway: granular refusal eliminates the binary choice between delivering everything and delivering nothing.

The three stages are decomposition, per-claim attribution, and selective refusal. Decomposition parses a generated response into discrete, individually verifiable assertions before any attribution check begins. Per-claim attribution matches each assertion to a specific passage in an organization-controlled source corpus — not merely to a document covering the general topic. Selective refusal then applies configured policy to each claim that fails attribution. All three stages must be present; skipping any one collapses the architecture to a weaker verification model.

Attribution checks applied to whole responses or whole paragraphs cannot detect intra-sentence hallucinations. A system skipping decomposition and checking responses as units is performing topic-level plausibility scoring, not claim-level grounding. The verification pipeline is only as granular as its decomposition step. A response stating that a drug is approved for one condition and contraindicated in another must be split into separate claims, each treated as an independent verification unit. The takeaway: decomposition granularity determines the ceiling of the entire verification system.

The deploying organization controls the approved source corpus — not the model and not the verification layer itself. Claim-level grounding limits what the model can assert to what the approved corpus can support, rather than asking the model to be accurate in a general sense. In a legal deployment, claims must resolve to specific case citations or statute passages; in a clinical deployment, to specific sections of approved clinical guidelines. The takeaway: the corpus boundary is an organizational governance decision, not a technical default.

Selective refusal policy is configured by the deploying organization based on the risk profile of the use case — it is not a technical default set autonomously by the verification layer. In a high-stakes clinical deployment, policy may require full refusal if any single claim is ungrounded. In a lower-stakes research context, policy may allow delivery of grounded claims with the ungrounded portion withheld and flagged. A verification layer that makes this decision without organizational policy input is not performing governed AI output management.

No. Claim-level grounding is explicitly a pre-delivery architecture. Post-delivery review, audit logging, or human-in-the-loop correction after an answer has reached an end user does not satisfy the architectural requirement. The verification pipeline must sit between model output and the delivery endpoint so that the end user never receives an ungrounded claim. Logging that an error occurred after a clinician or attorney has already acted on it does not prevent harm. The takeaway: interception timing — before delivery, not after — is a structural requirement, not a preference.

In a legal deployment, per-claim attribution requires each assertion to resolve to a specific case citation or statute passage — not merely to a document that covers the general legal topic. A model that retrieves a relevant case but then generates a citation that does not appear in that case will fail attribution at the claim level and trigger selective refusal before the answer reaches the attorney. This is structurally different from a system that retrieves documents and trusts the model to cite accurately. The takeaway: legal AI reliability requires claim-level attribution to specific passages, not document-level retrieval.

In a clinical deployment, each factual assertion — such as an approved indication, a contraindication, or a dosing parameter — must resolve to a specific section of an approved clinical guideline, not merely to a document covering the drug or condition. A response about a drug's approval status and contraindications is decomposed into separate claims, each verified independently. Policy in high-stakes clinical contexts typically requires full refusal if any single claim is ungrounded. The takeaway: clinical AI safety requires sub-sentence verification granularity, not document retrieval.

A genuine claim-level system must demonstrate that it can pass a response where most claims are grounded while suppressing a single ungrounded sentence — topic-matching systems cannot do this because they evaluate responses as units. It must also show that a retrieved document whose general topic matches the query but whose text does not contain a specific assertion causes that assertion to fail attribution. Systems that conflate retrieval with verification will pass that assertion. The takeaway: ask vendors to demonstrate selective suppression of a single ungrounded claim, not just overall accuracy rates.