A small internal-tools team is building a wiki-search assistant so employees can ask questions in their own words, such as how do I get my badge replaced, and still find the relevant article even though its title reads Building Access Card Replacement Procedure. Plain keyword matching keeps missing these because the wording differs from the article text. Which capability should the retrieval layer use to find articles by meaning rather than exact wording?

The correct answer is vector embeddings that represent text meaning for semantic similarity search. Vector embeddings convert text into numerical representations of meaning, so a query like how do I get my badge replaced can be matched against an article titled Building Access Card Replacement Procedure based on semantic similarity rather than requiring shared keywords, which is exactly the gap this team is running into. The choice that raises the maximum output token limit is incorrect because raising the maximum output token limit only allows longer generated responses, it does nothing to help the retrieval step find articles whose wording differs from the user's query. The choice that lowers temperature is incorrect because temperature affects how varied the wording of a generated response is, it has no role in matching a search query to relevant documents by meaning. The choice that applies a stricter content safety filter is incorrect because content safety filtering blocks inappropriate or unsafe queries and responses, which is unrelated to the problem of paraphrased queries failing to match article titles. Since the core issue is that meaning-equivalent phrases are not being recognized as related by exact-text matching, the fix is a retrieval method built to compare meaning rather than literal wording, which is what embedding-based semantic search provides.