Fine-tune stable formatting behavior: Which design best fits? | AIGP
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Fine-tune stable formatting behavior: Which design best fits?

AIGP Understanding How to Govern AI Deployment and Use Hard

Use fine-tuning for stable format behavior and retrieval for daily operational facts.

The question

A retailer needs demand forecasts with a stable company-specific output format, while promotions and inventory levels change daily. Fine-tuning data is incomplete and retraining is slow. The design must support current inputs without assuming either method guarantees factual accuracy. Which design best fits?

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  1. Fine-tune monthly and retrieve only exceptional operational updates.
    Monthly retraining is too slow for daily changes, and exceptional-only retrieval leaves ordinary current inputs unsupported.
  2. Fine-tune stable formatting behavior, then retrieve daily inputs. ✓
    Fine-tuning addresses the stable presentation behavior, while retrieval supplies changing promotions and inventory information at forecast time.
  3. Use retrieval alone and accept variable formatting across forecasts.
    Retrieval can provide current inputs, but accepting variable formatting fails the stable company-specific output requirement.
  4. Use fine-tuning only.
    Fine-tuning alone cannot reliably supply daily promotions and inventory levels, which are changing operational facts.
The trap
Separate stable behavior learned by the model from current facts that must be retrieved and verified.

How to remember it

Use fine-tuning for stable format behavior and retrieval for daily operational facts.

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