Chunking in RAG: why your splitter matters more than your embedding model

<p>Package #5 in my small open-source series, and the first one with a direct RAG/AI angle.</p> <p>Every RAG pipeline starts the same way: take a long document, split it into

<p>Retrieval-Augmented Generation (RAG) has become the default architecture for building AI-powered document intelligence systems. Most implementations follow the same

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<p><strong>Semantic Chunking</strong><br /> Lets Consider two paragraphs A and B, focussing on strings in python. para A focus on typecasting and para B focus on accessing