How to Compare Keyword and Vector Search on Business Questions

Key takeaways

• Vector search represents queries and content as numeric vectors, then uses distance or similarity to find nearby items.

• Identify the relevant document for each question in advance.

• One average score can hide exact-code failures.

Finding similar meanings does not guarantee an exact product-code match. Instead of asking which search method is smarter, compare what each one misses on the questions your team actually asks.

For the underlying concept, see What is vector search? in the AI glossary.

Separate identifiers from semantic questions

Keyword search can be useful for exact terms and identifiers, while vector search supports similarity-based retrieval. Azure’s hybrid search is one implementation combining them. Judge evidence retrieval on business questions rather than declaring a universal winner.

Create questions with known relevant documents

A fictional support team can include error codes, feature descriptions, symptoms, and comparisons between similar products. Have an owner identify the relevant document for each question. Include natural user phrasing and spelling variations, not just document titles.

Separate retrieval tests from answer tests

First check whether candidate results contain the needed clause. Then assess whether the answer uses it correctly. Do not collapse missing evidence and misinterpreted evidence into one undifferentiated score. Record latency and result length separately.

Understand failures before combining methods

If product codes work but natural-language symptoms fail, target that category first. Hybrid retrieval and reranking add configuration and operating considerations. Keep documents, permissions, and questions matched, and record wrong-product or obsolete-version results separately.

A worked business example

Consider a support team searches for “error E-17” and “screen freezes after payment.” The first needs an exact identifier; the second involves varied descriptions of a symptom.

Identify the relevant document for each question in advance. Test keyword, vector, and hybrid retrieval with matched documents and access, separating evidence retrieval from final-answer correctness.

Failure conditions to check

One average score can hide exact-code failures. Compare wrong products, missing clauses, and latency by question type.

A checklist for your workflow

Frequently asked questions

Is vector search sufficient for exact codes?

Test representative codes and confusion with similar identifiers.

Related AI concepts

• What is vector search?

• What is an embedding?

• What is retrieval-augmented generation (RAG)?

Sources and editorial information

Microsoft: Hybrid search

TENCONIC editorial team · Structure reviewed October 28, 2026. Official sources explain concepts and implementation examples; features vary by product. Scenarios and checks are editorial recommendations, not customer results or product promises.