Extract Data From Text with AI
Free text in, named fields out. The extraction prompt pattern that turns any unstructured text into consistent, parseable records.
Overview
Asking a model to "pull out the important information" produces different fields every run. Reliable extraction means naming the fields, describing what each one holds, and deciding two things up front: what happens when a value is missing, and how much inference is allowed. This resource loads a lead-form extraction — free text from a contact form into six named CRM fields, strict ambiguity, null for anything absent — the smallest complete example of the pattern that works on any text.
Workflow
-
Name the fields, not the wish
Replace "important details" with lead_name, email, use_case — fields the consumer can rely on existing.
-
Decide the missing-data behavior
Null keeps keys stable for pipelines; empty or "unknown" keeps gaps visible for humans. Decide once, in the prompt.
-
Set the ambiguity policy
Strict for data you'll act on, best guess for data you'll review. The default drift between the two is where inconsistency comes from.
Why This Works
- Named fields with descriptions turn a vague request into a checkable contract
- Explicit missing-data rules eliminate the model's biggest improvisation point
- The example extraction shows the exact shape, so the model imitates instead of inventing
Best for
- Any pipeline that feeds model output into code or a spreadsheet
- Teams tired of extraction results that change shape between runs
- Texts with no fixed layout — notes, messages, form dumps
Not for
- Defining the output format in depth — that's the JSON Output Prompt Builder
- Assigning labels from a fixed set — that's classification, not extraction
Use cases
- Turning free-text form submissions into CRM-ready records
- Getting the same six fields out of every text, every run
- Replacing "summarize the key info" prompts with named-field extraction