Embedding
A list of numbers that represents the meaning of a piece of text, so that similar meanings sit close together in vector space. The same model must embed both the corpus and the query.
What is Embedding?
A list of numbers that represents the meaning of a piece of text, so that similar meanings sit close together in vector space. The same model must embed both the corpus and the query.
Embedding is a advanced concept that sits in the LLM and GenAI Ops area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space, not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition, with diagrams, code, and a quiz to lock it in, work through the "Vector Databases and ANN" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
Learn Embedding in depth
Full interactive lesson with diagrams, code examples, real-world references, and a quiz.
Open the Vector Databases and ANN lessonRelated lessons
Lessons that touch on Embedding as part of a larger topic.
Embedding Storage
How to efficiently store, manage, and serve high-dimensional embedding vectors at scale
intermediate · database types storage
Vector Database
Databases built for AI, storing and searching high-dimensional embeddings that power semantic search, RAG, and recommendation engines
intermediate · database types storage
Design a RAG System at Scale
Design a production Retrieval-Augmented Generation system - vector databases, chunking strategies, retrieval pipelines, reranking, hybrid search, caching, evaluation, and cost management
capstone · capstone
See also
Related glossary terms you might want to look up next.
Vector Database
A database optimized for storing and querying high-dimensional vectors. Powers AI applications like semantic search and recommendation systems.
RAG (Retrieval-Augmented Generation)
A pattern that retrieves relevant text from an external index and puts it in the model's prompt so the answer is grounded in current, verifiable facts rather than the model's memory.
ANN (Approximate Nearest Neighbor)
Search that finds vectors close to a query without scanning every item, trading a small chance of missing a true neighbor for a large gain in speed. HNSW and IVF are the common indexes.
Cosine Similarity
A measure of how close two embeddings are, based on the angle between them rather than their length. The standard way to rank how related two pieces of text are in vector search.
HNSW
Hierarchical Navigable Small World, a graph-based nearest neighbor index that gives high recall at low latency by navigating layers of links, at the cost of more memory than IVF.
Reranking
A second retrieval stage where a cross-encoder scores each candidate chunk against the query directly. Slower than the first-stage retriever but far more precise, so it runs only over the top few dozen results.