A Detailed Introduction to a Novel Chunking Method for Enhancing the RAG Workflow

Retrieval Augmented Generation (RAG) systems present a viable solution to hallucinations by grounding the LLM's generation on contextually relevant documents. The segmentation of textual content, often into 'chunks,' plays a crucial role in these systems, directly impacting retrieval quality.

Traditional chunking methods, which rely on sentences or paragraphs as basic units, often fail to capture the true semantic boundaries within the text. Imagine trying to retrieve a specific detail from a lengthy novel. If the text is divided into rigid, equally sized chunks, we may end up with segments that either contain incomplete context or are filled with irrelevant information, reducing the efficiency of information retrieval.