What are embeddings in AI?

The plain idea

Computers are good at comparing exact things — is this string identical to that one — and bad at comparing meaning. Embeddings close that gap. An AI model reads a piece of content and converts it into a vector: a fixed-length list of numbers that encodes what the content is about rather than the exact words or pixels used to express it.

The useful property is distance. Two pieces of content that mean similar things produce vectors that sit close together; two that mean different things produce vectors that sit far apart. “King” and “queen” land near each other; so do two sentences that ask the same question in different words, even if they share almost no vocabulary. That single property — meaning as distance — is what makes embeddings so widely useful.

Why they matter

Before embeddings, comparing two pieces of text by computer usually meant comparing their exact words — which fails the moment someone phrases the same idea differently. Embeddings let a computer compare things by meaning instead, which is the basis of semantic search (finding results that answer a question rather than matching its keywords), recommendation (finding items similar to ones a user already liked), clustering (grouping similar items together automatically) and retrieval (pulling the most relevant passages from a large collection).

This is a genuinely general-purpose building block. Once content is represented as vectors of meaning, a wide range of problems reduce to a much simpler one: finding the nearest points in space.

How embeddings power RAG and vector search

Embeddings are the mechanism underneath two techniques covered elsewhere on this blog. Our explainer on retrieval-augmented generation describes retrieving relevant passages before asking a model to answer — and embeddings are what makes that retrieval step work by meaning rather than exact wording. Our explainer on vector databases describes the specialised storage that holds large numbers of embeddings and finds the nearest ones to a query efficiently. Put simply: embeddings are what a vector database stores and compares, and what a RAG system searches over before it ever calls the model that writes the answer.

How embeddings are created

Embeddings come from an embedding model — a model trained specifically to convert content into vectors of meaning, distinct from a chat model trained to hold a conversation or write text. You feed content in, whether a sentence, a document chunk or an image, and the embedding model outputs its vector. The same embedding model must be used consistently for both the content being stored and the queries searching it, so that “near” means the same thing on both sides of the comparison.

Practical uses

Beyond RAG, embeddings show up wherever “find things like this” matters:

• Semantic search — returning results that match a query’s meaning, not just its keywords.
• Deduplication — spotting near-identical records, listings or support tickets phrased differently.
• Recommendation — surfacing items similar in meaning to ones a user already engaged with.
• Classification and clustering — grouping free-text content into topics or categories automatically.

The honest limits

Embeddings are not a neutral, objective map of meaning — they reflect the data and the model that produced them. If the training data under-represents a language, a dialect or a domain, that content embeds less reliably; if the training data carries biases, the geometry of the resulting space can too, an association can sit closer than it should. This is the same territory covered in our explainer on responsible AI, and it applies just as much to the “invisible” infrastructure of embeddings as it does to a model’s visible output.

Embeddings also carry no built-in explanation — a vector’s numbers do not tell you why two items are close, which makes auditing an embedding-based system harder than auditing a rule-based one.

Embeddings in certification study

Embeddings sit inside the generative-AI and retrieval material on the AWS Certified AI Practitioner (AIF-C01), typically alongside RAG and vector search rather than as a standalone topic — the exam expects you to know what an embedding is conceptually and why it enables semantic retrieval, not the mathematics behind it. For developers building on these patterns, the AWS Certified Developer – Associate is the natural next step, where embeddings become an implementation detail of wiring retrieval into an application.