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Agreed - unpopular languages and packages have pretty shaky outcomes with code generation, even ones that have been around since before 2023.
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Neither RAG nor loading the docs into the context window would produce any effective results. Not even including the grammar files and just few examples in the training set would help. To get any usable results you still need many many usage examples.
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My own 100% hallucinated language experiment is very very weird and still has thousands of lines of generated examples that work fine. When doing complex stuff you could see the agent bounce against the tests here and there, but never produced non-working code in the end. The only examples available were those it had generated itself as it made up the language. It was capable of making things like a JSON parser/encoder, a TODO webapp or a command line kanban tracker for itself in one shot.
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And yet it works well enough, regardless. I have a little project which defines a new DSL. The only documentation or examples which exist for this little language, anywhere in the world, are on my laptop. There is certainly nothing in any AI's training data about it. And yet: codex has no trouble reading my repo, understanding how my DSL works, and generating code written in this novel language.
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