Foundations
What Are Embeddings? How AI Represents Meaning as Numbers
Embeddings turn words, sentences, or images into lists of numbers positioned so that similar meanings end up near each other — the foundation search and RAG are built on.
Turning meaning into geometry
An embedding is a list of numbers (a vector) that represents a piece of text, image, or other content in a way a model can do math with. The key property: content with similar meaning gets mapped to nearby points in that numeric space, so "dog" and "puppy" end up close together while "dog" and "spreadsheet" end up far apart.
Why this is useful
Once meaning is geometry, comparing two pieces of content becomes a distance calculation instead of a text-matching problem. That's what powers semantic search (finding results that mean the same thing, even with completely different words) and is the retrieval step behind RAG systems.
Where embeddings live
Embeddings are usually stored in a specialized vector database built to search millions of these number-lists quickly for the nearest matches to a query — a very different kind of lookup than a traditional keyword-based database performs.
Frequently Asked Questions
What is an embedding in AI?
A list of numbers representing a piece of content, positioned so that content with similar meaning is mathematically close to other similar content — the basis for semantic search and RAG.
Why not just search by keyword instead?
Keyword search misses results that use different words for the same idea. Embedding-based search matches on meaning, so a query for "affordable laptop" can still find a result about a "budget notebook."