What is semantic search in RAG?

Semantic search retrieves information based on the *meaning* of a query rather than requiring documents to contain exactly the same words. In RAG, it helps identify relevant knowledge-base chunks to send to the language model as evidence.

How does it work?

1. Index documents: Split source documents into meaningful chunks.
2. Create embeddings: Convert each chunk into a vector using an embedding model.
3. Embed the question: Convert the user's query into a compatible query vector.
4. Find neighbors: Compare the query vector with indexed vectors using a metric such as cosine similarity or dot product.
5. Retrieve context: Return the most relevant candidate chunks, optionally filter and rerank them, then pass selected evidence to the generator.

The document and query embeddings must share a compatible representation space; otherwise similarity scores are not meaningful.

Example

A company policy says: "Employees may work remotely up to three days per week."

A user asks: "How often am I allowed to work from home?"

A keyword-only search may struggle if it relies heavily on literal overlap. Semantic search can retrieve the policy because *working from home* and *working remotely* express closely related ideas.

The RAG system then supplies the original policy text to the LLM, which can answer using that evidence.

Semantic search versus keyword search

• Semantic search: Strong at matching paraphrases, related concepts, and questions expressed in different wording.
• Keyword search (for example, BM25): Often better for exact identifiers, error codes, product names, and rare technical terms.
• Hybrid search: Combines semantic and keyword results when neither approach alone is reliable enough.

Semantic similarity is not proof that a chunk answers the question. A close vector neighbor may still be irrelevant, outdated, or contradictory.

What affects search quality?

• Embedding model: Does it represent the domain's vocabulary and intent accurately?
• Chunking: Are answer-bearing facts kept together with enough context?
• Similarity metric and index: Are vectors compared consistently, and does approximate search retrieve strong candidates?
• Metadata filters: Does search stay within the correct documents, language, version, and authorized tenant?
• Reranking: Can a second stage improve the ordering of candidates?
• Evaluation: Do relevant evidence chunks appear in Top-K across representative questions?

When is semantic search useful in RAG?

Use it especially when users naturally paraphrase documents, ask conceptual questions, or use different terms than the stored text. Consider hybrid retrieval when precise literal matches are just as important.

Interview takeaway

Semantic search in RAG embeds both document chunks and user queries into a compatible vector space, then retrieves chunks with similar meaning. It makes retrieval robust to paraphrasing, but it should be combined with appropriate filtering, evaluation, and sometimes keyword search or reranking to deliver reliable evidence.