Retrieval pattern guide / 2026
Search and Extract for LLMs: The Two-Step Retrieval Pattern in 2026
Combining discovery and page reading into one opaque call makes it hard to understand why an agent answered a question. A two-step pattern makes the sources, evidence, and synthesis inspectable.
Quick answer
Discovery and reading are different operations.
Search returns a shortlist. Extraction turns a selected URL into readable content or typed data. Separating the two saves context for evidence.
curl --get 'https://webstractor.com/api/extract' \
--data-urlencode 'url=https://example.com/' \
--data-urlencode 'format=json'Available data
What you can extract
- Search result feeds
- Selected page content
- Source URLs through every stage
- Markdown or semantic JSON
AI workflows
Where normalized data helps
- Question answering
- Research agents
- Product comparison assistants
- Web-grounded developer tools
AI-ready output
Markdown for models. JSON for systems.
Raw HTML consumes tokens on navigation, scripts, styling, and interface labels. Clean Markdown keeps the readable hierarchy for LLM prompts and RAG chunks. Normalized JSON is better when your application needs an explicit semantic type, source, author, publication date, media, attributes, and collection items.
Always retain the canonical URL from the response. AI-generated summaries should remain traceable to the public source, especially when the underlying page can change.
Boundaries
Public data only
- Search snippets are not complete evidence.
- Extraction does not crawl linked pages.
- Prompts still need source-selection and citation instructions.
webstractor.com does not bypass CAPTCHAs, login walls, paywalls, access controls, or regional restrictions. Review the source’s terms and applicable law before collecting or reusing data.
Ready to try it?