Embedding models are the quiet muscle behind modern LLMs. In a sentence, they are how LLMs fundamentally understand text. They don't get the hype of the frontend models and agents, but nevertheless they are worth understanding — here's how they work, what they do, and why they matter.
Core Idea
At a high level, an embedding model converts text into raw data that can be processed by the rest of an AI system. Specifically, it turns words, sentences, or documents into a vector — a list of numbers. Similar sentences will produce similar vectors, while dissimilar ones will have vectors that are far apart in mathematical space.
For example, the two sentences "buy red running shoes" and "purchase crimson slippers" will share some features in their vectors — those associated with buying things and those associated with footwear — while differing on others, such as exercise versus comfort. This is an extremely powerful tool that extends well beyond text: images, audio, and even user behaviour can all be expressed in vector form.
Not Just a Dictionary
Traditional software was only able to match when things were exactly the same. Ask it "was there a sentence about red running shoes?" and it would return only an exact match — nothing more.
Embedding models change this. They allow a system to return an exact match confidently, while also making a weaker suggestion that the crimson slippers sentence might also be relevant. This enables more intelligent search, smarter clustering, and — critically for Zero Point AI — searchable memory for AI agents. It is essential for robust, modern AI to let machines reason with nuance rather than rely purely on binary matching.
Embeddings in RAG
A critical application of embedding vectors is in databases. By comparing multiple vectors, it becomes possible to look up content by meaning alone, regardless of whether specific words appear. Rather than relying on exact matches, these databases can search by cosine similarity — asking whether the embeddings point in broadly the same direction — by Manhattan or Euclidean distance, which asks whether the vectors occupy roughly the same space, or by statistical methods that identify shared themes across multiple embedding vectors.
This is the backbone of RAG systems, semantic search, and AI-powered knowledge bases. Zero Point AI's Sleuth Agent uses cosine similarity to provide a robust and reliable way of finding relevant context for your queries.
Common Problems
Technical teams working with embeddings will typically need to navigate three key challenges. Chunk size is the first: too large and retrieval loses precision; too small and important context falls between the cracks. Choosing the wrong similarity metric is another common pitfall, leading to ranking issues and missed relevant content. Finally, ignoring embedding type — using a model whose embedding space doesn't suit the task — can quietly degrade results across the entire system.
The Takeaway
Embedding models allow businesses to turn unstructured data into valuable statistics, build AI systems that can refer intelligently to their own data, and decouple searches from specific word matching. The result is AI that understands meaning — not just keywords.
Could your organisation benefit from a Retrieval Augmented Generation product or a semantic search engine that goes beyond specific word matching? Contact us at [email protected] to discuss with an expert.