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Context quality over model or connector metrics
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Context quality over model or connector metrics
When evaluating enterprise AI platforms, teams should prioritize context quality—whether the system grounds every step of every workflow in current, relevant, permissions-aware knowledge—over traditional metrics like model quality, context window size, or connector count.
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That context layer is the piece most enterprise AI evaluations still overlook. Teams compare model quality, context window sizes, and connector counts. All of these are important, but once AI moves from answering one-off questions to helping with research, decisions, drafting, and execution, the harder problem is context quality — whether the system can ground every step of every workflow in current, relevant, permissions-aware enterprise knowledge.
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