Title: Hey HN, I&#x27;ve been using GPT a lot lately in some side projects around data generation and benchmarking. During the course of prompt tuning I ended up with a pretty complicated request: the value that I was looking for, an explanation, a criticism, etc. JSON was the most natural output format for this but results would often be broken, have wrong types, or contain missing fields.<p>There&#x27;s been some positive movement in this space, like with jsonformer (<a href="https:&#x2F;&#x2F;github.com&#x2F;1rgs&#x2F;jsonformer">https:&#x2F;&#x2F;github.com&#x2F;1rgs&#x2F;jsonformer</a>) the other day. But nothing that was plug and play with GPT.<p>This library consolidates the separate logic that I built across 5 different projects. It lets you prompt the model for how it should return fields, inject variable prompts, handle common formatting errors, then cast to pydantic when you&#x27;re done for typehinting and validation in your IDE. If you&#x27;re able to play around with it, let me know what you think.
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