The DSL paradigm is generally how I go about using LLMs on new projects, I.e use the LLM to design a language that best represents the abstractions and concepts of the project - and once the language is defined, the LLM can express usecases with the DSL and ultimately convert them into an existing high level language like Python.
That is s great idea. I’ve used ChatGPT to help me define the names of the functions of an API. Next time I face a problem where it calls for DSL I will give it a try.
Earlier an HN user had given an example of using Prolog as an intermediate DSL in the prompt to an LLM so as to transform English declarative -> Imperative code - https://news.ycombinator.com/item?id=41549823
In general, we already have plenty of mechanisms for specifying interfaces/api specs, tests, relationships, etc in a declarative but more formal manner than natural language which probably all work , and I can only imagine we will continue to see the development of more options tailored to this use case.
