Choosing between knowledge graphs and vector databases is a false choice. Knowledge graphs excel at structured relationships and reasoning, while vector databases handle semantic similarity and unstructured data. The companies getting real value from AI are using both together, and here is how to decide which approach fits your specific problem.
Most RAG systems fail at retrieval, not generation. The language model can only be as good as the documents you give it. Learn how chunking strategy, hybrid search, and proper evaluation determine whether your RAG system works in production or joins the 70% that fail.
RAG implementations cost 2-3x initial estimates. Infrastructure expenses, development overhead, and operational costs nobody mentions in sales demos. 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.
Choosing between Pinecone, Weaviate, and ChromaDB matters less than you think. Your embedding strategy will make or break performance, not your database choice. 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.