Grounding guide / 2026

How to Build Source-Grounded AI Answers in 2026

Grounding is a workflow: find sources, read relevant content, distinguish source facts from model interpretation, and retain the URLs that support an answer.

Quick answer

Make the source URL part of the answer contract.

Retrieve a small set of relevant sources, pass their readable content to the model, and return canonical URLs beside the generated answer.

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

Available data

What you can extract

Canonical URLs for attribution
Published and modified dates when exposed
Readable content for review
Semantic metadata for answer policies

AI workflows

Where normalized data helps

Cited research assistants
Customer-facing answer systems
Compliance-sensitive summaries
Human review queues

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

A URL does not prove every generated inference.
Conflicting sources should remain visible.
High-stakes decisions need domain review.

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.