Using predicted outputs for proofreading

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I provided the same analysis.

• It always costs more
• It should be costing OpenAI less
• There is token billing overlap in prediction hits and misses
• Speed is slower without majority context matching

Conclusion: If you don’t want to always pay more for dubious speed benefit in an arbitrary or even targeted application - don’t.

When OpenAI predicted outputed input content is large, the effect is average?

The missing guide: Employing predictions at all begins with a speed penalty baseline. The most minimal of prediction placed will lower the token production rate. This continues for a long time into actual application. A document that is an exact match with the most minor of changes, such as “bold some words” receives a rate increase, but comes with rejected token costs in excess of the alterations. I tried many possible scenarios to find and test the utility, and while situations like the lat…