Most teams building Retrieval-Augmented Generation (RAG) systems follow the same playbook: chunk the data, generate embeddings, store them in a vector database, and connect everything to an LLM. On paper, it’s a solid pipeline. In practice, something feels off. The answers aren’t wrong, but they’re not quite right either. This gap is often blamed on […]
