AI-Ready Data

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RAG Chunking and Parsing: Proven Fixes for Better Answers

Retrieval systems are judged on their answers but built on their chunks. This guide covers the two steps that create them: parsing, which turns a file into usable text, and chunking, which cuts that text into the units your index stores. It compares fixed, recursive, structure-aware and semantic splitting, sets out workable size and overlap ranges, explains the metadata that turns a search result into a citable answer, handles the document types that defeat generic splitting, and shows how to measure retrieval with a gold question set before you trust it.

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AI-Ready Data Checklist: Essential Fixes to Avoid RAG Risk

Retrieval pilots rarely fail because the model was wrong; they fail because the content underneath it was duplicated, stale, unparseable or wrongly permissioned. This checklist sets out what to fix before building RAG or agentic workflows on your own data: a source inventory with named owners, permission-aware indexing, duplicate and stale content sweeps, format and chunking fixes, the metadata retrieval actually depends on, governance and retrieval-level logging, and a gold question set that turns quality from an opinion into a measured trend. It closes with a realistic first month, the mistakes that stall programmes, and answers to the questions buyers ask most.

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