Tag: vector-databases
Knowledge graphs vs vector search: Why the hybrid approach wins
Choosing between knowledge graphs and vector databases is a false choice. Knowledge graphs excel at structured relationships while vector databases handle semantic similarity, but the HybridRAG study shows combining both delivers measurably better accuracy on complex queries. Here is how to decide which approach fits your specific problem.
Building RAG systems that actually work in production
Most RAG systems fail at retrieval, not generation. Research from Anthropic and kapa.ai confirms the retrieval layer matters most. Chunking strategy, hybrid search, and proper evaluation determine whether your RAG system works in production or joins the 70% that fail.
The hidden costs of RAG: Why your budget is 3x too low
RAG implementations cost 2-3x initial estimates. Benchmarkit found 85% of organizations misestimate AI costs by more than 10%. Vector databases, embedding APIs, development time, and ongoing optimization add up quickly. Learn what teams consistently underestimate and how to budget accurately from day one.
Vector databases: Pinecone vs Weaviate vs ChromaDB
Choosing between Pinecone, Weaviate, and ChromaDB matters less than you think. Your embedding strategy will make or break performance, not your database choice. With the vector database market projected to more than triple, most companies spend weeks comparing databases when their embedding model barely works. Learn why embedding quality determines success and how to actually choose the right vector database for your needs.