Topic

Embeddings

7 posts on embeddings — part of glossary on the n4n AI blog.

What are embeddings in AI, explained simply?

A practitioner's guide to embeddings — what they are, how vector representations capture semantic meaning, and why they power search, RAG, and recommendation systems.

July 28, 20266 min read

GlossaryGuide

How text embeddings turn words into vectors

A practical guide to how text embeddings work, covering model selection, dimensionality tradeoffs, indexing strategies, and common pitfalls when moving from prototype to production.

July 28, 20265 min read

GlossaryGuide

How embeddings power semantic search

A practical guide to building semantic search with embeddings — model selection, indexing strategies, query processing, reranking, and production pitfalls.

July 28, 20264 min read

GlossaryDefinition

How cosine similarity measures embedding closeness

A precise technical explanation of cosine similarity for embeddings — how it works, why magnitude invariance matters, practical code, and the misconceptions that trip up engineers.

July 28, 20265 min read

GlossaryComparison

Embeddings vs tokens: what's the difference?

Understand the core difference between embeddings and tokens, when to use each, and how they interact in LLM pipelines.

July 28, 20266 min read

GlossaryGuide

Embeddings and RAG: how retrieval actually works

A practical guide to how embeddings power retrieval in RAG systems, covering vector search, chunking strategies, hybrid retrieval, and common production pitfalls.

July 28, 20264 min read

GlossaryListicle

Common embedding model mistakes that hurt search quality

Six embedding model mistakes that silently degrade search quality, with code patterns to detect and fix each one.

July 28, 20264 min read