Using AI · Growth Library
Extract a Table with AI, Then Sample-Check the Rows
Turn repeated text into structure without confusing extraction with ground truth
Try this
Define the columns and allowed values before extraction. Ask the model to return null rather than guess, preserve a source snippet or locator for each row, and flag ambiguous cases. Check a random sample plus all high-impact rows against the original text before using the result downstream.
Check-in: Do I know the observed extraction error rate well enough for the consequence of the next step?
Evidence: Practical guidance; no evidence-like claim is detected in the current source record.
Review note: Practical information-extraction workflow with traceability, visible uncertainty and consequence-weighted verification; no generic accuracy rate is claimed.
LLMs are convenient format converters. Convenience becomes dangerous when a guessed value and an extracted value look identical in the final spreadsheet.
1. Freeze the schema first
Define field names, types, allowed values, and what counts as missing. Keep the schema smaller than the information you merely wish existed.
2. Preserve traceability
Request the exact source phrase, paragraph, or locator behind each extracted row. This makes review possible without rereading the entire corpus.
3. Make uncertainty visible
Tell the model to return null or an uncertainty flag when the source is ambiguous. Do not reward completion at the expense of truth.
4. Sample-check before downstream use
Verify randomly selected rows and all high-impact fields. Record error types. If failures are patterned, repair the prompt, schema, preprocessing, or tool before continuing.
Questions to consider
Why require null instead of a guess?
Missing data is visible and can be reviewed. A plausible invented value looks complete and can travel much farther before someone notices it.
How much should I sample?
There is no universal percentage. Increase verification as error cost rises, and fully verify fields that trigger high-impact actions. If the sample reveals systematic errors, stop and fix the extraction process rather than sampling harder.
Maintenance record
Review history
Current status: Reviewed. An editorial or evidence review is recorded and no later event changes that conclusion.
2026-09-15 · reviewed · Brali editorial agent Source: review-registry-using-ai-starters.json
A practical extraction workflow with traceability, explicit missingness, and consequence-weighted verification instead of unsupported claims about model accuracy.
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Implementation observations can trigger a review, but they cannot change an evidence conclusion by themselves. Commercial relationships do not control review status or outcomes.
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Article versions
Brali keeps substantive article history visible. Later reviews may refresh wording, sources, examples, or boundaries without silently replacing the record.
- Version 2026-09-15: first recorded publication in the migrated Brali corpus.
- Evidence review 2026-09-15: reviewed by Brali editorial agent; evidence state is
practical.
Canonical source record · Evidence state: practical.