Big proponent of guaranteed outputs for LLMs. I wrote a library awhile back (gpt-json) that did something similar by querying the OpenAI API. At the end of the day though while their responses are _highly likely_ to be valid JSON they're not guaranteed. There's only so much that can be done with remote calls to their model's black box.
The future here really lies in compiling down context free grammars. They let you model json, yml, csv, and other programming languages as finite state machines that can force LLM transitions. They end up being pretty magical: you can force value typing, enums, and syntax validation of multivariate payloads. For use in data pipelines they can't be beat.
I did some experiments a few weeks ago on training models to generate these formats explicitly with jsonformers/outlines. Finetuning in the right format is still important to maximize output. You can end up seeing a 7% lift if you finetune explicitly for your desired format. [^1] At inference time the CFGs will constrain your model to do what it's actually intended to.