You can also get LLM to optimize rules for a rules engine iteratively against some dataset.

It’s sort of like memoizing or distilling the knowledge. Works really well for certain type of problems.

> Calibration / Threshold Control

The amount of thinking is relatively calibrated. Ask an obvious classification, you get an instant answer. Ask a tricky one, much more thinking.

Shouldn’t we expect the LLM model in this case to already be trained on the test and train dataset?

Well timed article. :)

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