Watching your newly built Retrieval-Augmented Generation (RAG) chatbot confidently return an outdated FAQ page just because the keywords matched is a rite of passage for AI engineers.
A while ago, I experienced a specific failure mode while building a customer service agent for a client with multiple hospitality businesses, including a hotel and a beach resort. We realized that with standard vector RAG, segmenting their data into fixed-length text chunks completely disconnected the related content. As a result, the system consistently mismatched booking queries across the different entities.
This is known as the Chunk Mismatch problem. When you slice a complex document into arbitrary chunks to fit an embedding model's context window, you destroy its logical coherence. If an engineering ticket describes a bug at the top and the deployment solution at the bottom, standard text segmentation splits them apart.
Here is a technical breakdown of how to fix this by enforcing structure at write time using GraphRAG and Entity Resolution.
1. Structuring Data at Write Time: The Dual-Level Graph
