If you’ve built a RAG (Retrieval-Augmented Generation) system in the past year, you’ve probably hit the wall where your LLM returns confidently wrong answers, cites information that doesn’t exist, or completely misses relevant context sitting right there in your vector database. The problem isn’t your embedding model or vector store. Most RAG implementations treat context like a keyword search problem when it’s actually a meaning problem . Traditional RAG chunks documents, embeds them, retrieves the “closest” chunks, and feeds them to the LLM. In practice, this breaks down when chunks lose their surrounding context. A sentence like “It increased by 40%” is useless without knowing what “it” refers to or when this happened. Contextual retrieval explicitly preserves and leverages the relationships between chunks, their document structure, and their semantic meaning rather than treating each chunk as an isolated island of text.…