Retrieval Augmented Generation (RAG) Done Right: Chunking

Retrieval-augmented-generation (Grounded Generation) is a major architectural pattern for enterprise GenAI applications. Proper chunking of text data is crucial in this process, as it affects the performance of fact retrieval and summarization. Different chunking strategies, such as fixed-size chunking, recursive splitting, and NLP-powered chunking, can result in varying responses to user queries. Vectara's approach, which uses advanced natural language processing techniques to split documents into small enough chunks that capture a clean signal of semantic meaning, has been shown to be effective in most applications. This approach is coupled with the ability to include a broader context around matching chunks, providing a robust solution for many use cases. The open-source community is also heading towards similar strategies, such as LangChain's ParentDocumentRetriever and LlamaIndex's SentenceWindowNodeParser.

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