Embedding Model

The Embedding article described the result — vectors positioned so that similar meaning lands close together. This article covers the actual system that produces those vectors: an embedding model.

The simple definition

An embedding model is a trained neural network whose entire job is converting input — text, images, or other data — into an embedding vector. Recall from the Model article that a model is the trained artifact resulting from running an algorithm on data. An embedding model is exactly that, but built and trained for one narrow, specific purpose: not generating text, not classifying images into categories, just producing a well-positioned vector that captures the input’s meaning.

Why this is a distinct, separate kind of model

It might seem like any large language model could just double as an embedding model — after all, GPT and similar models already process text into internal numeric representations as part of generating a response. In practice, a model purpose-built for embeddings, trained specifically to place similar things close together, tends to produce more useful, more consistent embeddings than repurposing a general-purpose chatbot model for this narrow task. This is why real production systems typically use a dedicated embedding model, separate from whatever model generates the actual conversational responses.

flowchart LR
    A[Raw text or image] --> B[Embedding Model]
    B --> C[Embedding Vector]
    C --> D[Used for search, comparison, or retrieval]

ANALOGY vs. TECHNICAL REALITY

Analogy: Think of a specialized appraiser at an auction house, whose entire job is looking at an item and assigning it a precise, comparable value — not selling it, not describing its history in prose, just producing one consistent number that lets very different items (a painting, a vase, a piece of furniture) all be compared on the same scale.

Where this breaks down: An appraiser applies conscious expert judgment. An embedding model applies a fixed, trained neural network calculation — the same weighted-sum-plus-bias-plus-activation mechanics from the Node and Neural Network articles — to produce its output vector, with no judgment involved, just a learned mathematical transformation applied consistently to every input.

How an embedding model is actually trained

This connects directly to the Training Mechanics phase, applied to a specific goal. An embedding model is typically trained using pairs (or groups) of examples known to be related or unrelated — for instance, pairs of sentences that mean the same thing, versus random, unrelated sentence pairs. The Loss Function used during this training specifically penalizes the model when related pairs end up far apart in vector space, and when unrelated pairs end up too close together — pushing the model, through the usual Gradient Descent and Backpropagation process, toward exactly the “similar things close together” property described in the Embedding article.

What actually determines embedding quality

A few practical factors genuinely affect how useful an embedding model’s output turns out to be. The dimensionality — how many numbers make up each output vector — affects how much nuance the model can capture; more dimensions generally allow finer-grained distinctions, at the cost of more storage and slower comparison, a genuine trade-off covered further in the upcoming Vector Database article. The training data the embedding model saw shapes what kinds of similarity it’s actually good at recognizing — a model trained mostly on general web text may perform less well on highly specialized domains like legal or medical text than a model fine-tuned specifically for that domain.

A concrete example, layered

For a simple beginner example: a small embedding model trained on product descriptions might learn to place “wireless earbuds” and “bluetooth headphones” close together in vector space, despite sharing almost no words, because the training data repeatedly showed these terms used in similar contexts. For a production example: OpenAI offers text-embedding-3-small (producing 1,536-dimensional vectors) and text-embedding-3-large (producing 3,072-dimensional vectors) as separate, dedicated embedding models — confirmed in OpenAI’s own published API documentation — distinct from GPT-4 or GPT-4o, which are built and optimized for generating conversational text, not for producing embeddings; developers building a search feature would call the embedding model specifically, not the chatbot model.

How one embedding model learns “close” and “far”

During training, the model may see related and unrelated pairs:

Query:    "forgot my login password"
Positive: "steps to reset your password"
Negative: "how to change billing address"

The loss encourages the query vector to move closer to the positive document and farther from the negative document.

flowchart LR
    A[Query text] --> M[Same embedding model]
    B[Positive document] --> M
    C[Negative document] --> M
    M --> D[Query and positive closer]
    M --> E[Query and negative farther]

Query and document must use compatible encoding

Some embedding models use the same encoding instruction for both sides. Others are trained asymmetrically and expect prefixes or separate query/document modes. Follow the model’s documentation.

Never embed documents with one model and queries with an unrelated model. Their coordinates do not share a learned meaning, even if both output 1,536 numbers.

Real model example: quality, size, and cost are choices

OpenAI’s published comparison of text-embedding-3-small and text-embedding-3-large reports different retrieval benchmark results and costs. The larger model reported stronger MIRACL and MTEB averages, while the smaller model was designed to be more efficient.

Production selection therefore asks:

• Does it retrieve well on our documents and queries?
• Does it handle our languages and domain terms?
• How much storage will its dimensions require?
• What are embedding latency and operating cost?
• Can we re-embed the collection if the model later changes?

How OpenAI and Gemini embedding models carry out retrieval encoding

Both providers expose dedicated embedding models that return vectors rather than conversational answers.

flowchart LR
    A[Document or query] --> B[Provider tokenizer and embedding model]
    B --> C[Fixed-size dense vector]
    C --> D[Store or compare]

OpenAI’s text-embedding-3 family supports shortening output dimensions. Google’s current Gemini embeddings documentation documents gemini-embedding-2, flexible dimensions from 128 to 3,072, and task-specific formatting for retrieval queries and documents.

Gemini’s asymmetric retrieval guidance illustrates an important production detail: a search query and a stored document may use different task instructions while being encoded into a compatible space. Developers must use the documented pairing consistently.

Common misconception

A frequent beginner assumption: that you need a full, expensive, general-purpose language model to get useful embeddings, since embeddings sound like an advanced AI capability. In practice, dedicated embedding models are typically much smaller, faster, and cheaper to run than a full conversational model like GPT-4 — they’re doing one focused, narrower job, not generating fluent, open-ended text, which makes them significantly less computationally demanding per request, a genuine practical advantage for applications like search that need to process large volumes of text quickly.

Where this fits in what comes next

You now understand what produces embeddings and how. The next two articles, Dense Vector and Sparse Vector, cover the two fundamentally different shapes an embedding (or any vector) can take — a distinction that directly affects how these vectors get stored and searched in the articles that follow.

In one sentence

An embedding model is a neural network purpose-built to convert raw data into meaningful embedding vectors, typically smaller and cheaper than a general-purpose language model, and trained specifically to place related things close together in vector space.

Related Terms

• Embedding
• Model
• Loss Function
• Vector
• Dense Vector