What is an Embedding?
Embeddings convert words, sentences, or documents into arrays of numbers called vectors. These vectors position similar content close together in mathematical space. Words with similar meanings get similar vectors, which allows AI systems to understand relationships and perform semantic search.
How Embeddings Work
An embedding model processes input text and outputs a vector of floating-point numbers. This vector typically has hundreds or thousands of dimensions. Each dimension captures some aspect of the input's meaning.
The model learns these representations during training by processing large amounts of text. It adjusts the vectors so that semantically similar inputs produce similar vectors. Distance between vectors indicates semantic similarity.
For example, the embeddings for "dog" and "puppy" would be very close together, while "dog" and "skyscraper" would be far apart. This mathematical representation of meaning enables many AI applications.
"puppy" → [0.3, 0.7, 0.2, ...] (similar)
"skyscraper" → [0.9, 0.1, 0.8, ...] (different)
Why Embeddings Matter
Embeddings power semantic search. Traditional keyword search only finds exact word matches. Embedding-based search finds conceptually similar content even when different words are used. This dramatically improves search quality.
Embeddings enable retrieval augmented generation by helping systems find relevant documents based on meaning rather than keywords. This makes RAG systems much more effective at locating the right information.
Recommendation systems use embeddings to find similar items. Clustering and classification tasks rely on embeddings to group related content. Any AI task involving similarity or relationships benefits from embeddings.
Example of Embeddings
Consider a customer support system that needs to find relevant help articles. Here is how embeddings work:
User asks: "How do I reset my password?"
System process:
- Convert the question into an embedding vector
- Compare this vector against embeddings of all help articles
- Find articles with the closest vectors (most similar semantically)
- Return the most relevant articles
An article titled "Password Recovery Guide" would rank highly even though it does not contain the exact phrase "reset my password" because the embedding vectors are similar.
Common Mistakes with Embeddings
Using the wrong embedding model for your task causes poor results. Different models are optimized for different purposes. Some work well for short queries, others for long documents. Some are general purpose, others domain-specific. Choose a model that matches your use case.
Not normalizing embeddings before computing similarity can distort results. Most applications benefit from using cosine similarity on normalized vectors rather than raw distance calculations.
Ignoring the context window limits of embedding models leads to truncated input. If your text exceeds the model's maximum length, it gets cut off, potentially losing important information. Split long documents into chunks before embedding them.
Related Concepts
Embeddings are fundamental to retrieval augmented generation systems. RAG uses embeddings to find relevant documents semantically rather than through keyword matching.
Fine-tuning can be applied to embedding models to improve their performance on specific domains or types of content. This makes the embeddings more accurate for your particular use case.
LLM agents often use embeddings when they need to search through documents or compare semantic similarity as part of their decision-making process.