"What are embeddings in AI and machine learning"

"Embeddings are numerical representations of words, sentences, images, or other data that capture meaning and relationships. They're how AI systems understand that \"cat\" and \"feline\" are related, or that a customer complaint is similar to a previous one — by mapping concepts to points in a high-dimensional space where similar things are close together."

What Are Embeddings in AI? A Plain-Language Explanation for Non-Engineers

Embeddings are how AI systems represent meaning as numbers. A word, a sentence, an image, a product listing — any piece of data can be converted into a list of numbers (a “vector”) that captures what that data means and how it relates to other data.

If you’re evaluating AI tools, embeddings are the invisible layer that makes most of the interesting features work — semantic search, recommendation engines, RAG systems, and similarity matching. You don’t need to understand the math, but understanding the concept will help you ask better questions and spot vendor BS.

How They Work

Imagine you could place every concept in your business on a giant map. Similar concepts would be close together. “Customer churn” would be near “account cancellation.” “Revenue growth” would be near “sales increase” but far from “employee onboarding.”

That’s essentially what embeddings do, except the map has hundreds or thousands of dimensions instead of two. An AI model takes a piece of text and converts it into a list of numbers — typically 768 to 3,072 numbers — that represent its position in this high-dimensional meaning space.

The critical property: things that are semantically similar end up with similar numbers. “How do I reset my password” and “I can’t log in to my account” have very different words but very similar embeddings, because they mean similar things.

When a Business Would Use Them

You won’t use embeddings directly — they’re a building block inside other systems. But knowing where they show up helps you understand what you’re buying:

Search. Every modern AI-powered search system uses embeddings. Instead of matching keywords, the system converts your query and your documents into embeddings, then finds documents whose embeddings are closest to the query. This is why AI search finds relevant results even when the exact words don’t match.

RAG applications. When you build a system that lets an AI answer questions about your documents, the documents are stored as embeddings in a vector database. The user’s question is converted to an embedding, and the system retrieves the documents with the most similar embeddings.

Recommendation systems. Product recommendations, content suggestions, “similar items” features — these all work by finding items whose embeddings are close to items the user has interacted with.

Classification and clustering. Automatically categorizing support tickets, grouping similar customer feedback, identifying duplicate content — all driven by comparing embeddings.

What to Watch Out For

Embedding model choice matters. Different models produce different quality embeddings. An embedding model trained on general internet text may not understand your industry’s terminology well. If your AI search is returning poor results, the embedding model is the first thing to investigate — before blaming the rest of the pipeline.

Embeddings are frozen in time. An embedding model captures meaning as it understood it during training. If your domain uses terminology in unusual ways, or if language shifts over time, the embeddings may not capture the nuances you need. This is solvable — through fine-tuning the embedding model or choosing a domain-appropriate one — but it’s a gotcha that teams often discover too late.

Dimensionality isn’t free. Higher-dimensional embeddings capture more nuance but cost more to store and search. For most business applications, you don’t need the highest-dimensional model available. Match the embedding size to the complexity of your use case.

Garbage in, garbage out. Embeddings represent the meaning of whatever you feed them. If your source documents are poorly written, inconsistent, or full of jargon without context, the embeddings will reflect that confusion. Clean data produces better embeddings.

The Verdict

Embeddings are the foundational technology behind most useful AI features — search, retrieval, recommendations, and classification. You don’t need to understand the linear algebra, but you should know that the quality of your AI application depends heavily on the quality of its embeddings. When evaluating AI vendors, ask what embedding model they use and how they handle domain-specific terminology. The answers will tell you a lot about whether their solution will actually work for your data.