Update 07/2026: Two additions have become the production default since our last entry:
- Hybrid search + reranking as baseline: dense vectors combined with keyword search (BM25), followed by a cross-encoder/reranker step. Practice reports consistently show meaningfully better retrieval precision than pure vector search - make this the starting point, not an optimization.
- Agentic retrieval: retrieval as a loop instead of one-shot top-k - the agent reformulates queries, filters and re-retrieves until the context suffices (see the Agentic RAG section in Advanced & Modular RAG). This changes indexing priorities: good metadata and filterable structure matter more when an agent iterates.
Most current vector databases (Qdrant, Weaviate, Chroma) support hybrid search natively - the reranker is the piece you typically add yourself.
Vector databases have become essential for modern AI applications, particularly in Retrieval Augmented Generation (RAG) systems. This technique focuses on optimizing vector database usage for maximum performance, accuracy, and efficiency.
Vector databases excel in semantic search optimization within RAG pipelines, enabling efficient document retrieval and high-performance similarity search. They support both real-time vector search in AI applications and hybrid approaches combining vector-based similarity with traditional keyword-based methods.
Indexing:
Optimize Chunking
Smart chunking implementations focus on semantic coherence using recursive chunking with overlapping windows. Dynamic chunk sizing adapts to different content types, while metadata-rich chunking enables enhanced filtering capabilities. Prioritize semantic chunking with appropriate overlapping strategies.
Pipeline
Implement regular reindexing and data validation procedures
Embedding
Careful consideration of embedding models
Meta Data
Index Metadata, they enhance filtering capabilities
Retrieval
Use pre-filtering to reduce search spaces. See RAG Architecture Pattern for more details on different retrieval strategies.