LLM structured extraction means using an AI model to extract specific fields from text and return them in a fixed format.
The input is unstructured text. The output is structured data.
For example, from this text:
Jane Carter is VP Sales at Acme. She is based in Boston and manages enterprise accounts.
An LLM can return:
- Name: Jane Carter
- Job title: VP Sales
- Company: Acme
- Location: Boston
- Segment: Enterprise accounts
Structured extraction vs summarization
Summarization creates readable text.
Structured extraction creates fields you can filter, sort, review, and export.
Use structured extraction when the output must become spreadsheet columns:
- Names
- Job titles
- Companies
- Prices
- Dates
- Categories
- Technologies
- Locations
- Requirements
- Claims
- Source URLs
Good extraction prompts
Define the fields and the missing-data behavior.
Extract these fields from the text:
- Company name
- Product category
- Target customer
- Mentioned integrations
- Pricing mentioned: Yes or No
If a field is not present, return an empty value. Do not guess.
Text:
{{Page Text}}
This is related to structured LLM output, but extraction focuses on pulling facts from the input.
📌 Extraction is not research
Extraction should use the text you provide. If the model needs to search the web, use AI web research or an AI research agent.
LLM structured extraction in Datablist
Datablist lets you run structured extraction on CSV and Excel rows with Ask ChatGPT/OpenAI, Ask Claude AI, Ask Gemini, and other LLM enrichments.
For website content, use the Website AI Scraper or AI Agent. For stable HTML layouts, selector-based scraping may be better.
For broader context, read AI data extraction and AI data enrichment.