Embeddings Explained: What They Are and When to Use Them
Understand vector embeddings, similarity search, and practical use cases.
Embeddings Explained: What They Are and When to Use Them Embeddings are the quiet engine behind modern search, recommendations, and RAG systems. They turn text (or images) into vectors so you can measure similarity by math rather than keywords. This tutorial explains the concept and helps you decide when embeddings are the right tool. What Is an Embedding? An embedding is a numerical representation of content. Similar content produces vectors that are close to each other. That means you can ask: Which paragraph is most similar to this question? Which product description is closest to a user query? Which document is most related to a support ticket? How Similarity Works (In Plain Terms) Once you have vectors, you can compute distance. The smaller the distance, the more similar the content. Common similarity functions include cosine similarity and dot product. The exact math matters less than the idea: vectors closer together are more similar. When Embeddings Are a Great Fit Use embeddings when you need semantic search or grouping: Semantic search**: find relevant content even if the words are different Clustering**: group similar items automatically Recommendations**: suggest items that are close in meaning Duplicate detection**: find near-identical content When Embeddings Are the Wrong Tool Do not use embeddings when exact matching is required: IDs and codes** (invoice numbers, order IDs) Strict queries** (dates, case-sensitive fields) Regulatory or legal exactness** where only exact matches are allowed For these, traditional indexes are better. Building an Embedding Workflow A minimal embedding workflow looks like this: Normalize your text (remove noise) Generate...
Tags: Generative AI, Embeddings, Search