Retrieval Optimization

Prepares content for retrieval-augmented generation (RAG) systems, which split pages into chunks, embed them, and retrieve passages in isolation. A passage that only makes sense in the flow of the full page often fails retrieval or gets quoted out of context. This is a clarity layer — it helps every reader, human or machine.

Inputs

• Required: target URL(s) or pasted content
• Optional: the queries the content should be retrieved for

Data pulls

Fetch target pages with webfetch. To check which queries the pages are actually candidates for: