The model may confidently propose unsupported routes | AIGP
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The model may confidently propose unsupported routes: Which residual risk still most specifically requires

AIGP Understanding How to Govern AI Deployment and Use Easy

Fresh retrieval does not guarantee faithful output; the model may still generate unsupported route recommendations.

The question

A logistics company proposes replacing manual route scheduling with a generative AI system. It maps users, data access, dependencies, and controls. A pilot adds a timestamped traffic feed with freshness monitoring and retrieval. Which residual risk still most specifically requires attention?

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  1. The system may need additional cloud capacity.
    Capacity planning is a legitimate deployment concern, but it does not address whether generated schedules are supported by the available evidence.
  2. The model may confidently propose unsupported routes despite retrieval. ✓
    Retrieval and freshness monitoring improve available context but do not eliminate confabulation or guarantee faithful route recommendations.
  3. Dispatchers may need authority to approve route changes.
    Approval authority is an important operational control, but it is distinct from the residual risk that generated routes may be unsupported.
  4. The traffic feed may require a different vendor contract.
    Supplier terms may require review, but the stated freshness controls do not guarantee that generated route recommendations are faithful to the retrieved data.
The trap
Ask what a mitigation actually controls; retrieval does not by itself guarantee accurate or supported output.

How to remember it

Fresh retrieval does not guarantee faithful output; the model may still generate unsupported route recommendations.

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