What Are Embeddings in AI?

Quick Definition#

An embedding is a numerical vector representation of a piece of text, image, or other content. The embedding model converts the content into a list of floating-point numbers — typically hundreds or thousands of values — that captures the semantic meaning of the original content. The critical property that makes embeddings useful is that semantically similar content produces numerically similar vectors.

Embeddings are the foundation of semantic search, Retrieval-Augmented Generation (RAG), and agent long-term memory. They are what makes it possible for agents to find relevant information without exact keyword matching. For the infrastructure that stores and queries embeddings, see Vector Databases. Browse the full AI Agents Glossary for more related terms.

Why Embeddings Matter#

Traditional search relies on keyword matching: a search for "invoice payment" returns documents that contain those exact words. Semantic search powered by embeddings can also return documents about "bill settlement" and "outstanding balance" because the embedding model recognizes that these concepts are related, even though the words are different.

For AI agents, this capability is transformative. An agent answering a question about product returns should retrieve relevant documentation even when the user says "give back" instead of "return." An agent searching customer history should find relevant interactions even when the exact phrasing varies. Embeddings make natural language retrieval reliable at scale.

How Embeddings Work#

An embedding model — typically a transformer-based neural network — processes an input text and produces a fixed-length vector as output. The values in this vector encode the semantic content of the input in a high-dimensional space.

Example: The sentences "The dog chased the ball" and "A puppy ran after the toy" would produce vectors that are close together in embedding space because they describe similar concepts. "Quarterly earnings report" would produce a vector far from both of these.

The embedding model learns these relationships during training on large text corpora, developing an internal representation that captures semantic structure.

Popular Embedding Models#

OpenAI text-embedding-3-small#

OpenAI's text-embedding-3-small is one of the most widely used embedding models in production agent systems. It produces 1536-dimensional vectors and offers a strong balance of quality, speed, and cost. For most applications, it is the recommended starting point.

Dimensions: 1536 (can be reduced via Matryoshka representation)
Use case: General-purpose semantic search, RAG, memory
Cost: Inexpensive per token

OpenAI text-embedding-3-large#

The larger variant produces 3072-dimensional vectors with higher accuracy, particularly on complex reasoning tasks. Use it when retrieval quality matters more than cost or latency.

Sentence Transformers (open-source)#

Models from the Sentence Transformers library (e.g., all-MiniLM-L6-v2, all-mpnet-base-v2) are open-source embedding models that run locally. They are a good choice for teams that need on-premises deployment or want to avoid per-token API costs at high volume.

Cohere Embed#

Cohere's embedding models offer strong multilingual support and are a good option for applications serving multiple languages.

Embeddings in Semantic Search#

Semantic search is the primary use case for embeddings in agent systems. The workflow:

Ingestion phase:

1. Split the knowledge base into chunks (documents, paragraphs, or fixed-size text segments)
2. Generate an embedding for each chunk using an embedding model
3. Store each embedding alongside its original content in a Vector Database

Query phase:

1. Generate an embedding of the user's query using the same embedding model
2. Compute similarity between the query embedding and all stored embeddings
3. Return the top-k most similar chunks
4. Include retrieved chunks in the agent's context for reasoning

This is the complete RAG retrieval loop. The quality of retrieval depends heavily on embedding model quality, chunking strategy, and the choice of similarity metric.

Similarity Metrics#

Three common metrics measure similarity between vectors:

Cosine similarity: Measures the angle between two vectors, ignoring magnitude. Values range from -1 to 1, where 1 means identical direction. This is the most commonly used metric for semantic search and generally produces the best results for text embeddings.

Dot product: The product of two vectors. Related to cosine similarity but sensitive to vector magnitude. Often used in recommendation systems.

Euclidean distance: Measures the straight-line distance between two vectors in embedding space. Less commonly used for text embeddings.

For most agent applications, cosine similarity with text embeddings is the right default choice.

Embeddings vs. Fine-Tuning#

A common architectural question: should you use embeddings plus RAG, or fine-tune the model on your domain?

Use embeddings and RAG when:

• The knowledge base is large, specific, or frequently updated
• You need access to current information the model was not trained on
• You need to cite sources for retrieved information
• The task is primarily knowledge retrieval

Use fine-tuning when:

• The model needs to change its behavior or reasoning patterns
• The domain requires consistently specific output formats
• The task is not primarily about knowledge access

For most teams, embeddings plus RAG addresses knowledge-access needs, while Fine-Tuning for AI Agents addresses behavioral changes. These approaches are complementary and often used together.

Practical Considerations#

Chunking affects embedding quality: Long chunks contain multiple topics and produce averaged embeddings that may not capture any single concept well. Very short chunks lack context. A 200-500 token chunk with 50-token overlap is a reasonable starting point for most document types.

Embedding consistency: Use the same embedding model for both ingestion and query. Mixing models breaks similarity comparisons because different models produce different vector spaces.

Dimensionality reduction: Some embedding models support reduced-dimension outputs (e.g., OpenAI's text-embedding-3 models support Matryoshka representation learning that allows dimension reduction). Reduced dimensions lower storage and query costs at some accuracy cost.

Batch processing: Generate embeddings in batches rather than one at a time to maximize throughput and reduce API costs.

Embeddings and Agent Memory#

In addition to knowledge base retrieval, embeddings power agent long-term memory. The agent generates embeddings of interaction summaries, completed task records, and accumulated knowledge, stores them in a vector database, and retrieves relevant memories at the start of new sessions. This enables agents to maintain coherent behavior across interactions without reloading full conversation histories.

For implementation details, see Build an AI Agent with LangChain and Introduction to RAG for AI Agents.

Implementation Checklist#

1. Choose an embedding model and document it — this choice affects the entire retrieval pipeline.
2. Define a chunking strategy before generating embeddings.
3. Store embeddings in a vector database with metadata for filtered retrieval.
4. Test retrieval quality with representative queries before integrating with agents.
5. Monitor embedding generation costs and latency at production scale.

Related Terms and Further Reading#

• Vector Database
• Retrieval-Augmented Generation (RAG)
• AI Agent Memory
• Fine-Tuning for AI Agents
• Introduction to RAG for AI Agents
• Build an AI Agent with LangChain
• AI Agent Examples in Business

Frequently Asked Questions#

What are embeddings in AI, explained simply?#

An embedding is a list of numbers that represents the meaning of a piece of text. Similar texts produce similar numbers, making it possible to find semantically related content through mathematical comparison rather than keyword matching.

How do embeddings power semantic search?#

Both the query and the knowledge base documents are converted to embeddings. At search time, the query embedding is compared against all document embeddings by similarity score. The most similar documents are returned as results.

What is the difference between using embeddings and fine-tuning?#

Embeddings help the model access the right information at inference time. Fine-tuning changes how the model reasons and generates output. RAG with embeddings is for knowledge access. Fine-tuning is for behavioral change.