This approach may be too high-level "magic" to the point of being difficult to work with and iterate upon. Looking at the prompt templates (https://github.com/

This approach may be too high-level "magic" to the point of being difficult to work with and iterate upon. Looking at the prompt templates (https://github.com/bananaml/fructose/tree/main/src/fructose/templates https://github.com/bananaml/fructose/tree/main/src/fructose/... ), they use LangChain-esque "just try to make the output to be valid JSON" when APIs such as GPT-4 Turbo which this model uses by default now support function calling/structured data natively and do a very good job of it (https://news.ycombinator.com/item?id=38782678 https://news.ycombinator.com/item?id=38782678), and libraries such as outlines (https://github.com/outlines-dev/outlines https://github.com/outlines-dev/outlines) which is more complex but can better ensure a dictionary output for local LLMs.