Defined term
Embeddings
Numerical vectors that represent the meaning of a text, image, or other piece of content.
Embeddings are dense vector representations produced by a model trained to map semantically similar inputs to nearby points in vector space. They power semantic search, retrieval, clustering, classification, and deduplication. Embedding choice (model family, dimension, normalization) and indexing strategy directly drive retrieval quality in RAG systems.
When it matters
When you need to find semantically similar content from a large corpus. Required for any RAG system, recommendation engine, or duplicate detection workflow at scale.
Real example
A support knowledge base with 12,000 articles. We embed each article into a 1536-dim vector (OpenAI text-embedding-3) and store in pgvector. Each customer query is embedded and matched against the corpus using cosine similarity, returning top-5 in under 50ms.
KPIs to watch
Embedding precision@5 on labelled queries (>0.75 target), embedding refresh cycle (weekly for active corpus), corpus coverage (>95% of expected query topics).
Related terms
Vector store
A database optimized for similarity search over embeddings.
RAG (Retrieval-Augmented Generation)
Generation grounded in retrieved source documents rather than the model's parametric memory alone.
Semantic search
Search by meaning, not by keyword overlap.
Agentic AI
AI systems that can plan, take multi-step actions, and use tools to complete tasks autonomously.
See it in action
We use this every week
Book a 30-min call and we'll walk you through how Embeddings shows up in a real engagement we're running.
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