Glossary

Semantic search

Semantic search is a search method that finds results based on the meaning and context of a query rather than matching exact words. Instead of looking for documents that contain the specific words a user typed, semantic search identifies content that addresses the same concept, even if different vocabulary is used.

In customer service applications, semantic search powers help centers, knowledge bases, and AI-assisted support tools. It allows customers to describe their problem in natural language and receive relevant results, even when their phrasing does not match the language used in support documentation.

How semantic search works

Semantic search relies on vector representations of text. Both the query and the documents in the search index are converted into numeric vectors that capture semantic meaning. Queries and documents with similar meanings produce vectors that are mathematically close to each other, regardless of surface-level word overlap.

The process involves several steps:

Embedding: Text is passed through a trained language model that converts it into a high-dimensional vector. This is the foundation of semantic search and is closely related to vector embeddings, which power retrieval augmented generation (RAG) systems as well.
Indexing: Document vectors are stored in a vector database or search index optimized for similarity lookups.
Query processing: When a user submits a query, it is embedded using the same model, producing a query vector.
Similarity matching: The system identifies documents whose vectors are closest to the query vector using distance metrics such as cosine similarity.
Ranking and retrieval: The top-matching documents are returned, often with a relevance score.

Semantic search versus keyword search

Traditional keyword search relies on term frequency and exact matches. It works well when users know the precise vocabulary used in the documentation. It fails when customers describe problems in their own words, use synonyms, or ask questions in conversational language.

Semantic search addresses these gaps:

Synonym handling: A search for "cancel my account" surfaces articles about "account termination" or "closing your subscription" without requiring those exact phrases.
Conceptual matching: A query about "why is my bill higher than expected" returns pricing and billing articles even if neither phrase appears in the document titles.
Conversational queries: Customers can type full questions rather than guessing which keywords to use, and the system handles the translation to relevant content.
Cross-lingual capability: Advanced semantic models can match queries in one language to documents in another, supporting multilingual support operations.

Semantic search in AI-powered support

Semantic search is a core component of retrieval augmented generation (RAG) architectures. When an AI agent needs to answer a customer question, it uses semantic search to find the most relevant sections of a knowledge base, then generates a response grounded in that retrieved content. This approach reduces AI hallucinations because the system is answering based on actual documentation rather than generating from memory alone.

The quality of semantic search directly affects the quality of AI-generated responses. If the retrieval step surfaces irrelevant documents, even a well-designed language model will produce poor answers. Improving retrieval accuracy, through better embeddings, query expansion, or reranking, is often the most effective way to improve overall AI response quality in a support context.

Practical considerations for implementation

Organizations deploying semantic search for customer service should account for:

Knowledge base quality: Semantic search can find relevant content only if that content exists. Gaps in documentation will still produce poor results regardless of retrieval sophistication.
Index freshness: Product changes and policy updates must be reflected in the search index promptly. Stale documents produce confident but incorrect responses.
Evaluation: Search quality should be measured regularly using test query sets with known correct answers. Tracking metrics like recall and mean reciprocal rank helps identify degradation before it affects customers.

AWS's documentation on semantic search covers the technical infrastructure used in production systems. For how this capability fits within AI agent design, see the guide to AI agents.