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Make the agent treat every material statement as a record with three links: the exact evidence unit it uses, a local label such as source_supported or inference, and a validation verdict. Before an answer is rendered, software should reject unknown evidence IDs, claims that lack required support, and citations that fail a parser. Keep the retrieved passage, URL, retrieval time, and locator so a reviewer can inspect what the agent saw.
This is deterministic because the IDs, fields, labels, and acceptance rules are explicit and repeatable. It does not make semantic judgment infallible: deciding whether a passage really entails a claim can remain uncertain. Use a documented rubric, preserve the rationale, and send ambiguous or high-impact claims to human review instead of treating provenance metadata as proof that a statement is true.
What does a deterministic provenance pipeline do?
A provenance pipeline separates four jobs that are often mixed together: preserving source material, identifying evidence, drafting claims, and deciding whether the evidence supports each claim. The final answer is produced only after those records pass validation.
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| Stage | Required output | Failure action |
|---|---|---|
| Capture | Immutable source snapshot, URL, retrieval time, publisher or owner when available, and the passage used | Do not create a citable evidence record |
| Identity | Stable evidence ID plus a separate locator, such as block or line range | Reject duplicate or malformed IDs |
| Claim drafting | One material statement per claim record, label, evidence IDs, and wording | Require a label and an evidence policy |
| Support checking | Faithfulness, completeness, sufficiency verdicts with rationale | Fail or route to review |
| Rendering | Answer text with parsed citations that resolve to evidence IDs | Block publication |
OpenAI’s citation guidance treats citable units, source representation, citation syntax, and downstream parsing or validation as separate design components. Keeping them separate makes it possible to test each one.
How should an agent capture inspectable evidence?
Capture the material the agent actually relied on, not merely a page title or a search result. Store the source URL, retrieval timestamp, publisher or owner when available, and the exact passage, table block, or transcript segment used to draft the claim.
Choose a practical evidence granularity
Document-level evidence is easy to implement but often too broad for a reviewer. Line-range evidence is precise but fragile when formatting changes. Passage or block-level evidence is a useful default for many systems: it is narrow enough to inspect and usually more stable than line numbers.
| Evidence unit | Best use | Trade-off |
|---|---|---|
| Document | Claims about the existence or overall purpose of a source | Low precision; reviewers must search the whole document |
| Block or passage | Most factual statements and quoted specifications | Requires a reliable extraction boundary |
| Line range | Audits where exact layout is controlled | Can break when the source is reflowed or updated |
Preserve the retrieved text as an inspectable snapshot. If a page changes later, the stored passage lets a reviewer see the version that informed the answer. Metadata should describe when and where it came from; it should not be treated as a guarantee that the source is accurate.
Keep source text and metadata together
A minimal evidence record can look like this:
{
"evidence_id": "ev_2026_000184",
"source_url": "https://example.invalid/page",
"retrieved_at": "2026-10-26T14:30:00Z",
"publisher": "Example publisher",
"locator": {"type": "block", "value": "b17"},
"text": "Exact passage retained from the source",
"content_hash": "sha256:..."
}The URL above is only a schema illustration; use the actual URL in a real record. A content hash can detect accidental edits to a snapshot, while the locator tells a human where the passage appeared. Do not use a hash as a substitute for readable evidence.
How do stable IDs make claims traceable?
Assign every evidence unit a stable, unique ID and keep that identity separate from its locator. A block may move from one line range to another after extraction, but its evidence ID should remain the key used by claim records and citations.
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Rules for evidence IDs
- Generate IDs once when the evidence record is accepted; never derive them from a changing line number.
- Make IDs unique within the corpus or namespace, and preserve them across answer revisions.
- Keep the locator, URL, timestamp, and text as separate fields so each can be updated or checked independently.
- Retain superseded records rather than silently overwriting them; mark a newer snapshot as a replacement.
- Expose a human-readable evidence view so a reviewer can open an ID and inspect the exact passage.
This separation follows the practical distinction between stable identity and a locator. It also prevents a citation from becoming a decorative token that cannot be resolved after re-indexing.
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Define the labels as a local policy and publish the policy with the system. A useful two-label minimum is source_supported for wording directly supported by the cited passage and inference for a conclusion that combines, generalizes, or interprets evidence.
source_supported
Use this label when the cited passage states the material content in substance and the claim does not add an unsupported condition, quantity, causal link, or scope. A paraphrase can be source-supported; it does not have to be a verbatim quotation.
inference
Use this label when the agent reasons beyond what any passage explicitly says. Examples include combining two independently stated facts, predicting an outcome, assigning a likely cause, or recommending a design choice. An inference may have excellent evidence, but it remains an interpretation and should be written as one.
Optional local labels
Some teams add labels such as quote, calculation, conflict, or unresolved. There is no universal taxonomy mandated by the cited guidance. Whatever vocabulary you choose, document the evidence requirement and rendering behavior for every label.
A claim record should make the distinction machine-readable:
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{
"claim_id": "cl_0042",
"text": "Block-level evidence is a practical default for many citation systems.",
"label": "source_supported",
"evidence_ids": ["ev_2026_000184"],
"support_status": "pass",
"rationale": "The cited guidance recommends block-level citations as a good default."
}For an inference, require the same evidence links but state the reasoning explicitly:
{
"claim_id": "cl_0043",
"text": "Using block IDs will reduce review time in this workflow.",
"label": "inference",
"evidence_ids": ["ev_2026_000184", "ev_2026_000191"],
"support_status": "review",
"rationale": "The sources support block-level inspectability; the time-saving effect is a design inference."
}How can the pipeline test whether a citation really supports a claim?
Run three claim-level checks: faithfulness, completeness, and sufficiency. A citation being present is only a lookup check; it does not establish that the cited material supports the wording.
| Check | Question for the reviewer or evaluator | Typical failure |
|---|---|---|
| Faithfulness | Does the source support what the claim says without contradiction or distortion? | A source says “may,” while the answer says “will” |
| Completeness | Does the claim preserve the source’s important qualifiers, limits, and scope? | Dropping a regional, temporal, or conditional limitation |
| Sufficiency | Is the evidence adequate for the entire claim, or is more support needed? | One narrow passage used to support a broad conclusion |
NIST describes this probe pattern and says: “Each probe evaluates against trusted source material using a strict rubric, and returns a structured verdict. This verdict includes a rationale detailing exactly how the source supports or fails to support the claim based on the rubric’s criteria.” Apply the same structure to each material statement: verdict, rationale, and the evidence IDs examined.
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Translate each check into pass, fail, or review criteria. For example, faithfulness fails when the claim introduces a number absent from the passage; completeness fails when it removes an explicit exception; sufficiency becomes review when the claim combines several propositions but cites only one.
Do not force a binary answer when the semantic judgment is uncertain. A review verdict is safer than silently converting an interpretation into a fact. Store the evaluator’s rationale so a later reviewer can see why the result was reached.
What must happen before the answer is rendered?
Validation should run on structured records before formatting produces prose, links, or footnotes. Rendering is the last step, not the place where missing support is repaired.
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- Check schema fields. Require a non-empty claim ID, text, allowed label, evidence list, and support verdict for every material claim.
- Resolve IDs. Verify that every evidence ID exists in the retained corpus and belongs to the permitted source set.
- Enforce label policy. A
source_supportedclaim must meet the support threshold; aninferenceclaim must expose its reasoning and cannot be rendered with fact-only wording. - Run faithfulness, completeness, and sufficiency probes. Save the verdict and rationale, including the rubric version.
- Parse citation syntax. Confirm that every citation token follows the agreed grammar and resolves to one or more evidence IDs.
- Reject unsafe states. Block unknown IDs, absent required evidence, parser errors, failed checks, and unresolved high-impact claims.
- Render labels visibly. Show a fact or inference badge, or use an equivalent accessible marker, and let readers inspect the linked evidence.
A simple validator can express the hard gates:
for claim in claims:
assert claim.label in ALLOWED_LABELS
assert claim.evidence_ids or claim.label == "inference_unresolved"
assert all(eid in evidence for eid in claim.evidence_ids)
assert claim.support_status in {"pass", "fail", "review"}
if claim.label == "source_supported":
assert claim.support_status == "pass"
if claim.support_status in {"fail", "review"}:
block_rendering(claim.claim_id)The exact programming language and storage technology are implementation choices. The important property is that the rules are explicit, testable, and applied before publication.
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How should the system handle ambiguous or high-impact claims?
Route them to a human instead of weakening the label. Ambiguity is common when a passage is conditional, sources disagree, or a claim spans several evidence units.
Trigger review when
- The evaluator cannot decide whether the wording entails the source.
- The claim affects safety, finance, legal rights, privacy, or access to essential services.
- Sources conflict or one source is materially newer than another.
- The claim combines evidence from different contexts, populations, regions, or time periods.
- The evidence is indirect, truncated, machine-translated, or missing the surrounding qualification.
The reviewer should be able to edit the claim, change its label, add evidence, or reject it, while preserving an audit trail. Store the rubric version and reviewer decision so the same case can be rechecked after a policy change.
What does provenance prove—and what does it not prove?
Provenance describes origin, retrieval, and modification history. It does not establish that a source is true, that the publisher owns the underlying material, that the context was correctly interpreted, or that an unmarked statement was not generated by an AI system.
A valid citation can point to an inaccurate source. A complete snapshot can still be misunderstood. Conversely, a missing provenance signal is not evidence that content lacks a human author or was generated by AI. Keep provenance findings separate from truth, authorship, ownership, and intent judgments.
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How can you test the pipeline with a worked example?
Suppose a source states that block-level citations are a good default for many systems and separately explains that line ranges can be fragile when formatting changes. The agent drafts two records rather than one broad conclusion.
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- Fact record: “Block-level citations are a good default for many systems.” Label it
source_supportedand link the exact recommendation passage. - Fact record: “Line-range locators can break when formatting changes.” Label it
source_supportedonly if the passage states that limitation. - Inference record: “This implementation should start with block IDs and retain line ranges only for controlled layouts.” Label it
inference, link both passages, and record that the implementation choice is a synthesis. - Validation: Check that the first two records preserve qualifiers such as “many systems” and “can,” then mark the recommendation for review if the deployment context is unknown.
- Rendering: Display the first two as source-supported statements and the recommendation as an inference with an inspectable explanation.
This approach prevents a useful design recommendation from being mislabeled as something a source explicitly prescribed.
Which implementation choices should you decide locally?
The architecture can remain portable across programming languages, databases, and retrieval systems, but several policies must be explicit.
| Decision | Options to document | Why it matters |
|---|---|---|
| Evidence boundary | Document, block, passage, or line range | Controls precision and maintenance cost |
| ID scope | Per crawl, corpus, tenant, or permanent namespace | Determines whether old citations remain resolvable |
| Fact threshold | Direct entailment only, or paraphrase with bounded transformation | Prevents label drift |
| Inference display | Badge, sentence prefix, separate section, or API field | Lets readers distinguish interpretation from quotation |
| Review threshold | Confidence, impact category, source conflict, or evaluator disagreement | Allocates human attention consistently |
| Retention policy | Immutable snapshots, replacement chains, and deletion handling | Preserves an audit trail without ignoring source changes |
Write these decisions as versioned policy, not tribal knowledge. A policy update should identify which past claims need revalidation.
What should a production checklist include?
- Every citable unit has a stable ID, readable text, URL, retrieval time, and locator.
- Source snapshots are retained or their replacement history is recorded.
- Each material statement has exactly one primary label under the local taxonomy.
- Claims link to evidence IDs, and those IDs resolve in the evidence store.
- Faithfulness, completeness, and sufficiency are evaluated with a documented rubric.
- Evaluator verdicts include short rationales, not only a score.
- Citation syntax is parsed and validated before output is rendered.
- Failed and review-status claims cannot appear as unqualified facts.
- Inference wording exposes interpretation instead of imitating source language.
- High-impact, conflicting, or ambiguous cases reach a named review queue.
- Provenance reports are not presented as proof of truth, authorship, ownership, or intent.
Frequently Asked Questions
Can a citation validator tell whether a source is true?
No. It can verify that an ID resolves and that a passage meets your support rubric, but it cannot establish the source’s factual accuracy. Truth assessment requires separate source-quality and domain-review procedures.
Should inferred claims have citations?
Usually yes, when the inference depends on external evidence. Link the supporting passages, label the statement as an inference, and record the reasoning so readers can distinguish the source’s words from your system’s conclusion.
How do I handle a source that changes after an answer is published?
Keep the original snapshot and retrieval timestamp, assign the newer capture its own evidence ID, and mark the relationship between the two records. Revalidate affected claims instead of silently replacing the text behind an old citation.
Is one evidence passage enough for a long claim?
Only if that passage supports the claim’s full scope and qualifiers. If a sentence contains several propositions, split it into separate claims or attach enough evidence for each proposition; otherwise the sufficiency check should return review or fail.
Quick Recap
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