Embeddings
Embeddings convert text into vectors for semantic search and RAG retrieval. Structured content with definitions and FAQs is easier to retrieve and cite.
Definition
Embeddings represent text as vectors so systems can compare semantic similarity. They power semantic search and RAG. To make content more retrievable and citable, use clear structure, definitions, and answer-first passages.
Why it matters
- Core technology behind semantic search and RAG
- Enables retrieval by meaning, not just keywords
- Encourages structured, chunkable, citable content
How to implement
- Structure content into clear sections (one question per section)
- Provide glossary definitions and FAQs for coverage
- Keep content consistent with canonicals/hreflang to avoid fragmentation
FAQ
Common questions about this term.