Explore all 6 guides related to Missing Data in AI Extraction — Null, Unknown, or Skip.
6 guides
When a source is missing a field, AI tends to fill the gap with a plausible guess instead of saying it isn't there. Here's how to make the model mark missing data explicitly — and check the result before you trust it.
"Summarize this transcript" turns suggestions into decisions, invents deadlines, and drops owners. Here's how to extract decisions, action items, owners, deadlines, and open questions instead — each with evidence and a needs_review flag, and "not stated" for what's missing.
AI extraction returns clean JSON that quietly means something different each run. Design the extraction schema first — each field's meaning, type, allowed values, missing-value rule, and required evidence — and review it before a single record is pulled.
Ask AI to extract fields with "use null if missing" and one null means missing, false, a stated "none", and a real zero — valid JSON, wrong meaning. Handle unknown vs empty vs not-applicable in AI output: give each field a value state so absence gets classified, not collapsed.
You ask AI to fill a table — issue_type, priority, reason — and the same column gets filled by different logic each row: priority as urgency here, sentiment there. Here's how to define each field's meaning, allowed values, and include/exclude rules so AI fills them consistently.
A long AI chat ends with a dozen suggestions — some you accepted, some you rejected, some already done — and "list the to-dos" turns all of them into pending tasks. Here's how to extract action items sorted by state, tied to evidence, with finished work kept out of the queue.