How do embeddings actually capture meaning?
An embedding model reads a chunk of text and outputs a fixed-length array of numbers, typically hundreds to a few thousand dimensions. What matters is not any single number but the position it defines: “cancel my order” and “I want a refund” land close together, while “cancel” the movement verb and “cancel my order” land far apart, because the model learned meaning from context during training, not from keyword overlap.
Why do embeddings matter for retrieval?
Embeddings are the mechanism that makes RAG work: a query gets embedded, every document chunk was embedded ahead of time and stored in a vector database, and retrieval finds the chunks whose vectors sit closest to the query’s. A weak or mismatched embedding model is one of the most common reasons retrieval returns passages that look related but do not answer the question, reaching the LLM as thin, misleading context.
What actually decides embedding quality?
Model choice, chunk size, and domain fit. A general-purpose embedding model trained on web text underperforms on dense technical or legal language, and a chunk that is too long dilutes its own vector, burying the one sentence that mattered among ten that did not.