Embedding can be understood as converting text, images, audio, and other content into a string of digital vectors. AI can search by semantic not because it matches word by word like keyword searches, but because similar meanings tend to be closer in vector space.
A straightforward example
If a user searches for "how to return," the document says "Request after-sales refund process," which traditional keyword searches may not match; Embedding search will find two sentences with similar meanings, so place the relevant content first. This is the foundation for many knowledge base Q&A, recommendation systems, and similar image searches.
How to use Embedding in the system
A common approach is: first use the Embedding model to convert document fragments into vectors and store them in a vector database; When users ask questions, they convert the question into a vector and calculate its similarity to the database vector. The higher the similarity, the closer the semantics and the more likely it is to be recalled to the model or search page.
What does this have to do with large model answers?
Embedding itself is usually not responsible for writing answers; it is responsible for "finding relevant content." Large language models are responsible for understanding context and generating responses. RAG systems often feature embedding models, vector databases, resorting models, and generative models simultaneously; they are not the same thing.
Common misconceptions
First, Embedding is not always better with larger numbers; domain matching and evaluation are more important; Second, vector similarity does not mean factual correctness; it only indicates semantic similarity; Third, short texts, tables, code, and proper nouns may require special handling. When building a corporate knowledge base, the quality of embedding directly affects whether you can find the correct information.
