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Visiting Melto Mily(username: meltonemily753)
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Creation date: Nov 9, 2026 4:16am Last modified date: Nov 9, 2026 4:16am Last visit date: Nov 9, 2026 10:13am
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Nov 9, 2026 ( 1 post )
11/9/2026
4:16am
Melto Mily (meltonemily753)
A common problem with enterprise RAG systems isn't necessarily the language model. It's the information the retrieval pipeline feeds into it.
Imagine an internal AI assistant searching thousands of company documents. Every document is accurate, properly formatted, and approved. Yet the assistant retrieves an outdated policy because nobody tracked document versions or configured retrieval to prioritize current information.
Traditional data quality checks won't necessarily catch that.
For RAG applications, AI readiness also means reliable document retrieval, version control, meaningful context, and appropriate access permissions.
Curious how others approach this. Do you evaluate data readiness separately for RAG systems, or rely on your existing enterprise data quality framework?
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