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.…