Ask the same question of every document
Individual asks one question across every source in a dataset: one answer per source, directly comparable.
Individual is the simplest workflow. One question. Every document. One answer per document. Use it when you want directly comparable responses across a set: not summaries, not extractions, just the answer to this specific question from each source.
What you get
- One answer per source.
- Each answer is grounded in its source, with citations.
- Answers can be text or structured (depending on how you phrase the question).
- Downloadable as a per-source folder, a single combined PDF, or saved as notes.
Run it
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1
Build the dataset (a label, a multi-select, or a Smart Search result).
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2
Open the workflow runner and pick Individual.
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3
Type the question.
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4
Click Run.
What kind of question to ask
Individual works best with questions that have a clean per-source answer:
- "What's the governing law clause?": clear, document-specific.
- "Who is the principal investigator?": extractable from each paper.
- "What's the maximum file size accepted?": answerable from each spec.
Less well-suited:
- "Which of these is best?": that's a comparison, not a per-source question. Open a Focus instead.
- "Summarize the document.": that's Summarizer, which is purpose-built for summaries.
- "Extract these five fields.": that's Data Extractor, with type-safe schema.
If the question is really five questions, ask the most important one with Individual, then run again for the next.
Individual vs. a Focus on a label
This is the comparison that comes up most:
| Individual | Focus on a label |
|---|---|
| One question, N answers (one per source) | One conversation grounded in all N sources at once |
| Answers don't reference each other | Single composed answer spanning sources |
| Direct per-source comparison | Synthesis across the set |
Use Individual when the question is "what does each one say"; use Focus when the question is "what do they collectively say."
What's next
- Summarize a collection: for summaries instead of question-answers.
- Extract structured data: for typed fields across documents.
- Three ways to chat: Focus on a label for cross-source synthesis.