RAG Is Not Enough: The Rise of Enterprise Knowledge Graphs for AI Systems

Retrieval-augmented generation has become a standard pattern for grounding large language models in enterprise data. A typical implementation converts documents into embeddings, stores them in a vector database, retrieves the most similar chunks for a query, and adds those chunks to the model prompt. This works well for document lookup, policy search, support content, and other tasks where semantic similarity is the main requirement.