[parentheses]

The value is turn unstructured data into structured data and ensure it satisfies schema constraints.

For example: you have 1000 free-text survey responses about your product, building a schema and for-each `TypeChat`ing them would get you a dataset for that free-text. It's mind-bogglingly useful.

[verdverm]

yes, turning unstructured data into structured data is one of the most useful ways to use an LLM right now. It has been done before with using schemas and could be done without all the extra cruft.

There was a similar example a few months back using XML instead, but I haven't heard much about it since, because again, the library did not add value on top of doing these things in a more open or scripted setting.

MSFT has another project in similar vain, guardrails, interesting idea, but made worse by wrapping it in a library. Most of these LLM ideas are better as a function than a library, make them transform the i/o rather than every library needing to write wrappers around the LLM APIs as well

There are several more making use of OpenAPI / JSONSchema rather than TS.

We use a subset of CUE, essentially JSON without as many quotes or commas. The LLMs are quite flexible with few-shot learning. They can be made more reliable with fine-tuning. They can be made faster and cheaper with distillation.