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Why not train on summaries instead of full handover situations?

Summaries compress information, but handover tasks also require models to preserve uncertainty, distinguish evidence from interpretation and recognise what the receiving team needs.

**Is a good summary enough for a handover?**

No. A summary can accurately repeat the source material while omitting the distinction that matters to the receiving team. In insurance, a claims handler's view that damage is probably covered differs from a recorded coverage determination. Compressing both into 'coverage confirmed' changes the authority of the information.

**What should training material preserve?**

It should preserve the status of each important statement: reported by a customer, supported by evidence, inferred by a team or formally decided by an authorised role. It should also retain unresolved disagreements when those disagreements affect the next action.

**Why does the receiving team matter?**

A repair coordinator needs to know whether work may be authorised. An investigator needs to know which evidence remains contested. The same claim therefore supports different handover content without changing the underlying facts.

MirrorFirm could generate linked source records and recipient-specific tasks that make these differences explicit. A buyer can use them to train models to select relevant context without upgrading an assumption into a decision or erasing uncertainty for the sake of a cleaner narrative.

Assess whether a model transfers decision-relevant meaning rather than merely producing fluent compression.

Audience:
Model labs
Content type:
FAQ
Buyer question:
What it is and why it matters

Content release: r-a220eb6e099b243b

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