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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.

This process — reliably extracting text and splitting it into useful segments, or “chunks” — is where most RAG projects fail. Poor extraction leads to jumbled, nonsensical text, while suboptimal chunking can fragment key information, leaving your LLM without the context it needs to provide accurate answers. Getting this right means moving beyond naive text splitting and adopting a principled approach to document preparation. This is the playbook for turning your chaotic document repository into a source of clean, model-ready knowledge.

Why Document Ingestion is Your LLM Workflow’s Unsung Hero

Every RAG system relies on a simple premise: find relevant information from an external source and provide it to an LLM as context for answering a query. The entire system’s performance hinges on the quality of that retrieved information. If your pipeline loses or mangles text during the initial…