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What is semantic search?
Semantic search is a search method that looks for meaning—not just exact words. It is based on embeddings: texts are converted into vectors, whose spatial proximity reflects semantic similarity.
Core components: embedding model (converts text into vectors), vector database (stores vectors along with the original text), nearest neighbor search (finds the vectors most similar to the query), reranking (refines results using specialized models), hybrid search (combines semantic search with traditional full-text search).
Typical maturity levels: Pure full-text search (keyword-based), semantic search (embedding-based), hybrid search (combines both—standard by 2026), semantic search with reranking (two stages—fast retrieval plus precise reranking).
For small and medium-sized businesses, semantic search is the key to knowledge applications. Without it, there is no RAG, no intelligent support chatbot, and no modern document search. Off-the-shelf cloud services (Azure AI Search, Elastic with Vector, Pinecone) make getting started a pragmatic choice.
Best Practices for Semantic Search
These six principles have proven effective:
- Hybrid over Pure Semantic: Combining it with BM25 usually yields better results.
- Appropriate embedding model: German requires German or multilingual models.
- Optimize chunking iteratively: Too small—no context. Too large—precision suffers.
- Use metadata: Permissions and filters ensure relevant and authorized results.
- Use reranking: A 2- to 3-fold improvement in quality with relatively little effort.
- Monitoring: Incorporate user clicks and feedback into the optimization process.
Common Mistakes in Semantic Search
We often see these pitfalls:
- Incorrect chunk size: Too small or too large—results become unusable.
- Semantic only, no hybrid: Exact words aren’t found—a hybrid approach helps.
- Wrong embedding model: Using an English model on German texts — quality suffers.
- No reranking: Top results are often only “similar,” not “correct.”
- Data protection overlooked: Without authorization filters, users see what they shouldn’t.
Semantic vs. Full-Text Search vs. Vector Search
A comparison of three search approaches:
- Full-text search: Traditional—matches words. Fast, but blind to synonyms.
- Semantic search: Meaning—via embeddings. Also finds results phrased differently.
- Vector search: Technical term for semantic retrieval—similarity in vector space.
Implement Semantic Search with prodot
In a free initial consultation, we’ll review your knowledge base and outline a semantic search solution—as the foundation for chatbots, support, or knowledge management.
As an AI partner for small and medium-sized businesses, we implement semantic search in a practical way—using hybrid search, reranking, and a GDPR-compliant setup.
What We Offer
- AI consulting —search architecture and implementation.
- RAG in the glossary —the overarching approach.
- Embedding in the glossary —the mathematical foundation.
- Vector database — the storage for semantic search.
