Multi-source retrieval guide / 2026

How to Combine Multiple Public Sources for AI Context in 2026

More sources are not automatically better. A useful multi-source workflow is intentional: search finds candidates, extraction reads the few that matter, and the final answer separates supporting evidence from uncertainty or conflict.

Quick answer

Use multiple sources only when the question needs them.

Start with one strong source. Add another only to cover a missing dimension, check a time-sensitive claim, or represent a genuine disagreement.

curl --get 'https://extractor.sh/api/extract' \
  --data-urlencode 'url=https://example.com/' \
  --data-urlencode 'format=json'

Available data

What you can extract

Independent source records
Canonical links and metadata
Search feeds for discovery
Semantic JSON for orchestration

AI workflows

Where normalized data helps

Comparative research
Current-event synthesis
Product and market analysis
Evidence-aware chat interfaces

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

Do not fan out requests merely to create the appearance of coverage.
Conflicting sources need explanation.
Public sources can be incomplete or biased.

extractor.sh 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.