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# 5.5 Insight Generation and Narrative Layer

Raw model outputs are not yet insights. The AI engine therefore includes a narrative layer that translates numerical signals into explanations and recommendations that make sense to humans. This layer takes into account the user’s current context, recent behavior changes, and previously delivered messages, in order to avoid repetition and provide continuity.

For example, instead of reporting “utilization increased by 7% over 30 days”, the narrative layer may produce a message such as: “Your use of available credit has been trending higher over the past month, mainly on Card A, and is now above the range you maintained earlier this year. This makes you more sensitive to unexpected expenses.” Where appropriate, this is followed by one or two concrete options for action, framed in terms of trade-offs rather than prescriptions.

The narrative layer also governs alerting behavior. It decides when a change is sufficiently material to warrant a proactive message, and when it should be absorbed into periodic summaries instead. The goal is to preserve the user’s attention for events that matter, rather than training them to ignore notifications.


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