Definition
What this term means
A specialised database that stores and searches embeddings for fast semantic retrieval. Vector indexes are the infrastructure behind RAG systems. When an AI assistant needs to find relevant information to answer a query, it searches the vector index for content embeddings that are semantically closest to the user's question. Popular vector databases include Pinecone, Weaviate, and Qdrant.
Why it matters
The business impact
Vector indexes are the gatekeepers of AI retrieval. If your content is not indexed, or if it produces unclear embeddings, it simply will not be retrieved when users ask relevant questions. Ensuring your content is crawlable, well-structured, and produces clean semantic signals directly affects whether it appears in the vector indexes that RAG systems depend on.
Used in context
How you might use this term
“A SaaS company discovered that their dynamically-rendered product pages were not being indexed by AI crawlers. After implementing server-side rendering and structured data, their pages appeared in RAG retrieval results for the first time, driving a measurable increase in AI-sourced referrals.”
Related terms
Explore connected concepts
RAG
An AI architecture that combines real-time information retrieval with language generation. Instead of relying solely on pre-trained knowledge, RAG systems search external sources, such as websites, databases, or knowledge bases, to find relevant information before composing their response. This is the technology behind AI search tools like Perplexity and Google's AI Overviews.
Embeddings
Dense numerical representations (vectors) that capture the semantic meaning of text. When AI systems convert your content into embeddings, they create mathematical fingerprints that encode what your content is about, its context, and its relationships to other concepts. These vectors are used to measure semantic similarity, enabling AI systems to find content that is conceptually relevant to a query, even if it does not share exact keywords.
Semantic Search
A search approach that understands the meaning and intent behind a query rather than simply matching keywords. Semantic search uses NLP, embeddings, and knowledge graphs to interpret what a user is actually looking for, even if their query uses different words than your content. This technology powers both modern search engines and AI-assisted retrieval systems.