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You cannot guarantee that an AI system will never invent a detail, but you can make document extraction evidence-bound: require each value to point to a source location, return an explicit unknown when support is missing or unclear, and check consequential results against the document. A polished answer is not proof that it is correct.

Make every extracted value answerable to the document

Define the fields you need and their expected types, then instruct the system to use only the supplied document as evidence—not general knowledge. Tell it to return an explicit unknown or empty state when a field is absent, unreadable, or ambiguous. This is a workflow safeguard, not a guarantee that a model will follow the instruction perfectly.

Keep provenance beside each value: a page number, section, text span, or coordinates that let a reviewer locate the supporting passage. Microsoft describes source references and spans for checking extracted output against a PDF in its document analysis documentation. Exact features depend on the product and API version, so check the current documentation before choosing an implementation.

Verify that the source supports the value

Check the cited passage and surrounding context, not just whether the output is valid JSON or includes a confidence score. Neither a well-formed response nor a high confidence value establishes that a claim is true. Ask whether the source supports the value as stated, whether relevant context was omitted, and whether the evidence is sufficient. NIST describes these as distinct grounding dimensions: faithfulness, completeness, and sufficiency (NIST’s grounding evaluation guidance).

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Microsoft Support puts the practical rule plainly: “If a statement can’t be traced back to a source, treat it as unconfirmed until verified.” Check names, dates, numbers, quotations, citations, and any other consequential fields against authoritative source material. The Commons Library likewise advises careful checking, ideally with an expert, when using AI-generated content (Commons Library guidance).

Route uncertainty and high-impact fields to review

Make uncertainty a defined workflow state rather than an invitation to guess. Send missing, unreadable, ambiguous, contradictory, or unsupported values for review, and show the reviewer the original document beside the extracted result. Microsoft describes this approach as supporting human-in-the-loop validation (Microsoft document analysis guidance).

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Set review rules based on the consequences of an error, the uncertainty of the evidence, and performance on representative documents. There is no universal threshold in the cited guidance for when human review can safely be skipped. Government guidance warns that generative AI outputs are “statistically informed guesses rather than facts” and recommends documenting data sources and how a conclusion was generated (UK Government guidance on generative AI). The ICO also advises developers to assess and communicate the risks and impacts of inaccuracies (ICO guidance on AI risks).

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Test the whole extraction workflow

Evaluate with documents representative of the formats, layouts, languages, and conditions you actually process. Track more than invented values: omissions, transcription errors, lost context, and incorrect handling of uncertainty can also make an extraction unreliable. AWS recommends defining the veracity issues relevant to the use case and applying filters and verification (AWS guidance on mitigating hallucinations).

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When selecting a tool or designing a process, check whether it provides:

  • A verifiable source location for each output value.
  • A way to flag missing or low-confidence fields rather than fill them in.
  • Support for the document formats, layouts, and languages in your workload.
  • A side-by-side view of source and extracted result for reviewers.
  • Evaluation of both false values and omissions.
  • Auditability, access controls, and versioning appropriate to the documents.

Grounding makes errors easier to detect; it does not prove an extraction is correct. Reviewers must still judge whether the cited evidence supports the value and whether the output has lost important context. The sources cited here establish no universal hallucination rate for document extraction or percentage improvement from a particular control.

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