Weigh the tradeoffs

Larger models often raise quality and cost/latency together. Tiny models need more scaffolding. Switching vendors can win price and lose schema features.

Translate into user impact on the invoice-extraction service when tuning model selection. Which error class costs more—missed catches, slower answers, higher spend, or privacy exposure? That ranking picks the default more honestly than a blog’s recommended settings for model selection.

If model L hits 96% field-F1 at $0.12/1k docs and model H hits 97% at $0.55/1k, the +1 pt may lose unless errors are extremely costly. Scoped specifically to model selection / invoice-extraction service / tradeoffs.

If the aggressive model selection setting wins the headline metric while breaking a protected slice or blowing the latency budget on the invoice-extraction service, it is not a win. Record intended gain and tolerated regression together for model selection.

Revisit the model selection tradeoff when traffic shape changes on the invoice-extraction service. A setting that was right at low volume can fail when a new language segment or document length appears. Tradeoffs expire; re-measure on a calendar, not only on incidents.

Also pin one numeric memory from this model selection chapter: If model L hits 96% field-F1 at $0.12/1k docs and model H hits 97% at $0.55/1k, the +1 pt may lose unless errors are extremely costly. That number is not decoration; it is a template for how claims about model selection on the invoice-extraction service should look in design docs. Scoped specifically to model selection / invoice-extraction service / tradeoffs.

People confuse model selection with neighboring buzzwords when debugging the invoice-extraction service. Before changing prompts, ask whether the broken stage was evidence gathering, the model selection judgment itself, validation, or the product action. Fixing the wrong stage creates folklore (“we tried model selection and it failed”) that blocks the next team on the invoice-extraction service. Scoped specifically to model selection / invoice-extraction service / tradeoffs.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to choosing a model rather than generic AI advice.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to choosing a model rather than generic AI advice.

Write a five-line artifact for this page: goal, inputs, check, owner, stop rule. Invent one fluent failure that the check would catch. Keep details specific to choosing a model rather than generic AI advice.