Vector Search
Vector Search maps queries and documents into high-dimensional embeddings and retrieves by cosine distance — the mainstream recall layer in RAG and AI engines, complementary to keyword inverted-index search.
Category: Technical
What is Vector Search?
Vector Search maps queries and documents into high-dimensional embeddings and retrieves by cosine distance — the mainstream recall layer in RAG and AI engines, complementary to keyword inverted-index search.
How do you apply it in practice?
Implication: semantic paraphrases get recalled too, but paragraphs must be self-contained — orphan pronouns and choppy sentences hurt retrieval scores.
Why does it matter for AI search citations?
Generative search engines tend to extract sentences that are clearly defined, consistent and quotable on their own. Writing concepts like "Vector Search" as one consistent definition across your site, backed by structured data and verifiable sources, lowers the chance of being misread. This improves readiness only; it does not guarantee citations or ranking gains — measure actual citations to know.
Which related terms should you know?
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is the core architecture of answer engines: user query → vector-retrieve relevant pages → feed retrieved chunks to an LLM to synthesize the answer. This means content must be crawlable, chunkable, and embeddable.
Chunking
Chunking is the preprocessing step where RAG slices a page into 200–500 word fragments for vector indexing — usually along H2/H3, paragraph, and sentence boundaries. Chunker-friendly writing means clear subheadings + complete paragraphs = clean citable blocks.
Semantic Search
Semantic Search returns results by understanding intent and context, not literal keyword matches. Google BERT (2018) and MUM (2019) were the watershed. Content implication: cover complete concepts and synonyms, never keyword-stuff.
Vector Search at a glance
| Term | Vector Search |
|---|---|
| Category | Technical |
| Last reviewed | 2026-10-06 |
References
- Google Search Central: SEO documentation
- Schema.org structured data vocabulary
- W3C JSON-LD 1.1 Recommendation
- GEO: Generative Engine Optimization (arXiv paper)
These documents support the definitions and methods, not ranking or citation promises; reviewed as of 2026-10-06.
By the 23SEOGEO team · Last updated: