Key points
The article explains RAG as a controlled search-and-answer pattern, showing where source preparation, retrieval quality, citations, confidence handling, and human review matter to business use.
- RAG connects language models to approved internal knowledge
- It is useful when answers must be grounded in documents
- Good RAG still needs permissions, review, and source visibility
How i-360 applies this thinking
RAG can improve answer grounding by retrieving approved content before generation, but usefulness still depends on document quality, permissions, chunk structure, metadata, ranking, citations, and no-answer behavior.
Next step
Prepare approved documents, ownership and version rules, user groups, representative questions, unsupported questions, citation expectations, update frequency, and examples of sensitive answers requiring review.
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