Why are embeddings used in RAG?

Embeddings are used in RAG because they let the retrieval system compare meaning mathematically instead of relying only on exact keyword matches.

A RAG system needs to find document chunks that are relevant to a user's question. Embeddings make this possible by converting both stored content and incoming queries into vectors that can be compared in the same semantic space.

They help retrieve semantically similar content

Consider this query:

How long can I request a refund?

The knowledge base may contain:

The refund window is 30 days.

These sentences do not use the same words, but they express related meaning.

Keyword-only retrieval may miss such matches if the wording differs significantly.

Embedding-based retrieval can identify them because their vectors can be close in semantic space.

Documents are embedded during ingestion

When documents are prepared for RAG, their chunks are converted into embeddings.

Conceptually:

Document chunk
    ↓
Embedding model
    ↓
Document vector
    ↓
Vector index

The vector becomes a searchable representation of that chunk's meaning.

The user query is embedded too

When a user asks a question, the query is passed through a compatible embedding model.

User query
    ↓
Embedding model
    ↓
Query vector

The retrieval system then compares the query vector with stored document vectors.

Similar vectors indicate related meaning

Vector similarity functions such as cosine similarity or dot product can estimate how close the query is to each stored chunk.

A simplified flow is:

Query vector
    ↓
Compare with stored vectors
    ↓
Rank by similarity
    ↓
Return the most relevant chunks

The exact similarity method depends on the embedding model and vector index.

Embeddings reduce dependence on exact wording

This is important because users often ask questions differently from how source documents are written.

For example:

Query: How do I cancel my subscription?

Document: Customers may terminate their plan from the billing settings.

The wording is different, but an embedding model can still represent the semantic relationship between the two.

Embeddings make large-scale semantic search practical

A RAG knowledge base may contain thousands or millions of chunks.

Embeddings allow those chunks to be indexed in a vector search system so the application can retrieve a small set of likely relevant passages instead of sending the entire knowledge base to the language model.

They support the retrieval stage, not generation itself

Embeddings are mainly used to help locate relevant information.

They do not directly produce the final natural-language answer.

The typical relationship is:

Embeddings → retrieve relevant chunks
Retrieved chunks → provide context
LLM → generate grounded answer

Why use embeddings together with other retrieval methods?

Embeddings are strong at semantic matching, but they are not perfect for every query.

Exact identifiers, product codes, names, numbers, or rare terms may sometimes be better handled by keyword or sparse retrieval.

This is why some RAG systems combine embedding-based dense retrieval with keyword search in a hybrid retrieval system.

That topic is covered later in “What is hybrid search in RAG?”.

Compatible embeddings are essential

The stored document chunks and incoming queries must be represented in a compatible vector space.

If unrelated embedding models are used, their vectors generally cannot be meaningfully compared.

This is covered separately in “Why should document and query embeddings use compatible embedding models?”.

In short, embeddings are used in RAG to turn documents and queries into comparable numerical representations of meaning. This enables semantic search, helps retrieve relevant information even when wording differs, and allows large knowledge bases to be searched efficiently before the language model generates an answer.