The RAG Playbook: A Data Science Guide to Document Chunking

Author(s): The Bot Group

Originally published on Towards AI.

The RAG Playbook: A Data Science Guide to Document Chunking

Large Language Models are only as smart as the data we feed them. This is the fundamental challenge at the heart of modern data science, especially when building Retrieval-Augmented Generation (RAG) systems. While we marvel at an LLM’s ability to reason, its knowledge is trapped if it can’t access the information locked inside your company’s messy PDFs, Word documents, and scanned images. With an estimated 80% of the world’s data being unstructured, a robust ingestion and chunking pipeline isn’t just a nice-to-have; it’s the critical foundation of any successful AI application.

The article discusses the importance of a robust document ingestion and chunking pipeline in building effective Retrieval-Augmented Generation (RAG) systems. It highlights the challenges of extracting clean text from various document formats, such as PDFs, and emphasizes the need for strategic chunking to enhance retrieval performance. The author provides insights into different chunking strategies, including fixed-size, overlapping, section-based, and semantic chunking, and outlines best practices for creating a transparent and reliable machine learning pipeline.

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Published via Towards AI