A Correction Workflow for Evidence-Backed AI Reports
AI research products need a correction workflow before they need a confidence badge. A polished answer can still become wrong when a source changes, a citation is misread, or a synthesis step removes an important limitation. If corrections depend on an engineer manually editing the final page, the system cannot explain what failed or prevent the same error from returning.
A durable correction process should treat the published report as the end of a traceable chain, not as an isolated document.
Make correction intake specific
A generic contact form produces vague messages such as “this is wrong.” Ask the reporter to identify the exact claim, the reason it appears incorrect, and a supporting source when available. Preserve the submitted URL, report version, timestamp, and visible text because the page may change before review begins.
Do not require the reporter to prove the full replacement claim. A clear contradiction, dead citation, attribution error, or missing limitation is enough to open a review.
Freeze the affected claim, not the entire site
Corrections should be scoped. Link each published claim to its supporting evidence and the reports that reuse it. When a claim is challenged, mark that relationship for review and prevent automated reuse while leaving unrelated material available.
For a high-impact claim, temporarily add a visible review notice or remove it from recommendation and indexing surfaces. For a minor typo that does not change meaning, an ordinary edit may be enough. The response should match the potential harm.
Reconstruct the original decision
Reviewers need the state that existed when the report was published:
the original research question;
the queries that discovered the sources;
the retrieved source snapshots;
the exact excerpts mapped to the claim;
the model input and output used during synthesis;
the quality-gate result and reviewer actions.
Without this history, a correction becomes guesswork. Looking only at the current version of a source cannot show whether the source changed after publication or whether the pipeline misunderstood it from the beginning.
Classify the failure
Use a small, stable taxonomy. Retrieval failures include broken pages, redirect changes, extraction errors, and stale content. Evidence failures include weak authority, copied sources counted as independent, and excerpts that do not entail the claim. Synthesis failures include overgeneralization, removed qualifiers, and unsupported causal language. Publication failures include an incorrect title, missing uncertainty notice, or an index decision that ignored a policy gate.
Classification matters because each failure requires a different preventive change. Re-running the model will not fix a source-independence problem.
Decide with explicit outcomes
A review should end in one of several named states:
Confirmed: the claim remains supported and the report needs no factual change.
Clarified: the core fact remains, but wording or context must improve.
Corrected: the claim changes because stronger evidence contradicts or narrows it.
Retracted: the central claim cannot be supported responsibly.
Pending: the evidence is insufficient or inaccessible and the uncertainty must remain visible.
Store the reason and the evidence behind the outcome. Avoid silently replacing the text.
Publish a reader-facing correction
Readers should be able to see what changed, when it changed, and why. A useful correction note names the affected claim, summarizes the change, and links to the current evidence. It does not need to expose private operational data or every internal prompt.
Keep the original publication date and add a correction timestamp. If the title or conclusion changed materially, make that explicit. Corrections build trust only when readers can distinguish them from routine copy edits.
Propagate the result
A corrected claim may appear in several places: the original report, a summary card, a recommendation feed, cached search text, an email excerpt, or a later report that reused the same evidence. The evidence graph should identify those dependents and create a bounded update queue.
This is where complete provenance becomes operational infrastructure. The Omniracle editorial and indexing policy at https://omniracle.com/editorial-policy provides a public example of connecting evidence quality, uncertainty, corrections, and index eligibility instead of treating them as separate concerns.
Turn corrections into tests
Every meaningful correction should leave a regression test. If the system counted several syndicated pages as independent, add a test for common-origin detection. If a qualifier disappeared during synthesis, add a test that the final claim preserves scope and modality. If a risky topic passed without review, strengthen the pre-generation gate.
Track correction categories and time to resolution, but do not optimize for a low correction count. A system that discourages reports may appear perfect while remaining wrong.
Corrections are not an admission that evidence-backed publishing failed. They are part of evidence-backed publishing. The real failure is being unable to explain which claim changed, which evidence caused the change, and how the system will respond next time.
Omniracle's sourcing, AI disclosure, correction, professional-content, and publication standards.
