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I mean in my mind, the partial point of llm is that you don't control the output. You control the input.

Wanting an generative AI and wanting to cover what it says is like having your cake and eating it too

You want to control certain aspects of the output, and only leave the rest up to the GAI. The issue is that AI models don’t have a reliable mechanism for doing so.

That's not a fundamental limitation of the models, even if it's present in the products running on those models — if you want to populate a database from an LLM, you can constrain the output at each step to be only from the subset of tokens which would be valid at that point.

functions work fairly well for that https://platform.openai.com/docs/guides/function-calling

You control the output during training so no.

And even for humans, we have mechanisms to control their output when they get confused.

> And even for humans, we have mechanisms to control their output when they get confused.

What mechanisms do you mean? I don’t think it’s feasible to use hunger and fear of dismissal to control an instance of an LLM.

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