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.
