AI glossary for businesses
Embeddings
In short
Embeddings are lists of numbers that represent the meaning of a piece of text, an image or another item, so that a computer can compare items by meaning. Items with similar meaning get similar numbers, even if they use different words or languages. They power semantic search, recommendations and the retrieval step in RAG.
How embeddings work
Think of a map. Every shop on it has coordinates, and shops close to each other on the map are close in real life. Embeddings give every piece of text a set of coordinates, but in a "map of meaning" with hundreds or thousands of dimensions instead of two.
On that map, "running shoes", "αθλητικά παπούτσια για τρέξιμο" and "trainers for jogging" sit close together. "Running a business" sits far away, even though it shares a word. A special AI model, called an embedding model, creates these coordinates. Providers such as OpenAI, Google and Cohere offer embedding models, and open-source options exist too.
Once texts have coordinates, a computer can quickly answer questions like "which documents are closest in meaning to this question?" That is the core of semantic search and of RAG.
In practice for a small business
A Greek e-shop with kitchen and barbecue products has a search bar that only matches exact words. A customer types "δώρο για μπαμπά που του αρέσει το ψήσιμο" and gets zero results. With embedding-based search, the same query can return grill tools, aprons and spice sets, because their descriptions are close in meaning.
A second use: grouping customer reviews. Turn 2,000 reviews into embeddings, then cluster them. Reviews about "late delivery", "the courier never called" and "arrived after a week" fall into one group, even with different wording. See AI for customer review analysis for the full workflow.
You rarely need to touch embeddings directly. Many e-shop search apps, help-desk tools and AI assistants use them in the background.
Why it matters and what to watch out for
Greek quality varies: not every embedding model handles Greek, mixed Greek-English text or Greeklish equally well. Test with real customer searches.
Still personal data: embeddings of texts that contain personal data should be treated as personal data under GDPR. Deleting a customer's record should also delete the related embeddings.
Keep them updated: when a product or document changes, its embedding must be recreated.
Similar is not correct: the closest match is not always the right answer. Check results for important searches.