Ask for specific facts, fields, and passages from your source material, then review them in a consistent structure.
Intelligent document extraction uses AI to identify selected information in a document and arrange it for a particular task. Unlike a general summary, an extraction request names the details to retrieve: a study method, a product specification, a stated deadline, or a passage supporting a finding.
Start with clear field definitions and a focused set of sources. Ask iWeaver for the information you need in a table or organized list, with missing details left unresolved. Check the original material before using an extracted value in a report or decision.
“Find the date” is unclear when a document contains several dates. Ask for the publication date, event date, or deadline you actually need. A precise field definition gives you a clear way to decide whether the returned value is correct.
Ask for the relevant passage or available section reference alongside the extracted detail. This makes review easier when a number or statement depends on nearby context. Verify the reference itself rather than assuming that a requested citation must be correct.
A value without its unit, reporting period, or condition can be misleading. Include those attributes as separate fields when they matter. Retain wording such as an estimate or a range instead of turning it into an exact result.
Tell the AI how to represent a field that the source does not supply. “Not found” is more useful than an inferred detail when your goal is extraction. Keep a separate note for ambiguous wording that requires a reviewer’s attention.
For a research-paper comparison, define the fields before you read across studies. The sample below is an extraction specification, not a record of tested product output. Each field states what counts as an acceptable answer.
| Requested field | Definition | If the detail is absent or unclear |
|---|---|---|
| Research question | The question stated by the paper | Mark not found; do not invent an objective |
| Sample | The stated participants or observations | Keep missing size or population details unresolved |
| Method | The approach described in the methods section | Preserve qualifications about the study design |
| Main limitation | A limitation explicitly acknowledged by the authors | Separate an inferred limitation from extracted text |
Try: “Extract these fields from each paper. Use one row per source and include the supporting passage or section when available. Write ‘not found’ for absent details. Put any interpretation in a separate notes column.”
Select material relevant to one extraction task. Give files distinct names and list the fields you need. Explain the intended meaning of ambiguous terms such as date, result, owner, or total.
Request a table or list with one unit of information per row. Include a source column and define how missing fields should appear. Ask for original units and important qualifiers to remain attached to the extracted detail.
Review the source passage for key values and inspect ambiguous entries. Check that the value belongs to the requested field and source. Resolve uncertainty before combining rows into a briefing or another record.
Name the measures and reporting periods you need from each source. Ask for the value, unit, period, and supporting context in separate columns. Verify differences in scope before interpreting the figures as directly comparable.
Extract Report Facts
Request study questions, methods, samples, and stated limitations across your reading. Keep the extracted details tied to individual papers so you can inspect them later. Use the matrix to guide your review rather than to replace reading the studies.
Build an Evidence Matrix
Ask for explicitly agreed actions, named owners, and stated deadlines. Leave an owner or date unresolved when it was not supplied. Check that a suggestion discussed in the meeting has not been mistaken for an agreed commitment.
Review Meeting Actions
Define the attributes you want from each datasheet, including units and conditions. Ask for exact values and a note where the source uses a different test or measurement basis. Review those differences before building a side-by-side comparison.
Compare Specification DetailsConfirm that the value answers the field you defined. A due date is not an issue date, and a subtotal is not a final total. When several candidate values exist, keep the selection rule visible.
Check headings, footnotes, and qualifiers around the extracted passage. A table column may use a different unit or reporting period. Preserve those differences when preparing a comparison.
Keep missing and ambiguous entries separate from verified details. Automated document extraction needs an acceptance rule if the output feeds another process. A well-formatted row is a starting point for review, not evidence of correctness.
Choose a focused tool for the next part of your document workflow.
Get an overview before deciding which details deserve a closer extraction pass.
Understand the Document →Orient your reading before building a structured matrix of study details.
Review a Research Paper →Identify the main sections of a PDF before asking targeted questions.
Explore a PDF →Choose the source material, define a small set of fields, and ask for an output you can trace back to the original.
Start Extracting Information