Guidance is genuinely impressive for anyone wrangling LLM output. The ability to map grammar constraints so efficiently at inference solves so many subtle issues—tokenization headaches being just one. Curious if you've benchmarked adoption for JSON vs. custom grammars among production teams? Anecdotally, JSON's become the baseline, but custom grammars unlock way more nuanced applications.
Great question re: adoption...it's definitely dominated by JSON. Most API providers have standardized on JSON outputs, so application teams have started building shims that map other formats to JSON and back. Similarly, with models heavily being post-trained to generate "good" JSON, I think there's a better model-constraint alignment story with JSON than most arbitrary grammars.
That said, internally, we experiment quite a lot with custom grammars all across the stack. It's more complicated to write a grammar than a JSON schema (though LMs are very good at grammar writing now) and more error prone to debug, but it can help significantly in certain cases (e.g. having models write custom DSLs not commonly found on the internet, at various parts of a model training pipeline, etc. etc.). I'm hoping that with the right tooling around it, the broader community will start nudging beyond JSON.
To that end, the python guidance library is really an attempt to make writing grammars more friendly to a python programmer. More to be done here of course!
