Glossary
Semantic Search
Searching by meaning using embeddings, often combined with keyword search.
Semantic search finds content by meaning rather than by matching exact words. It compares the embedding of a query against the embeddings of stored passages, so a question phrased differently from the source text still surfaces the right answer. Hybrid search pairs this with traditional keyword matching to catch exact terms that pure semantic search can miss, such as product names, error codes, and SKUs.
CertifChat retrieves with a hybrid approach: semantic similarity for meaning plus keyword signals for precise terms, followed by a reranking step to order results by relevance. This combination is more robust than either method alone.
See how this works in practice across CertifChat's features.
Related terms
- EmbeddingsNumeric vectors that capture the meaning of text so similar passages sit close together.
- Vector DatabaseA store optimised for finding the embeddings most similar to a query vector.
- Retrieval-Augmented Generation (RAG)Grounding an LLM's answers in retrieved source documents instead of its training data alone.
- ChunkingSplitting documents into passages small enough to embed and retrieve precisely.
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