Dimensionality gets bizarre in 1000-D space. Similarity and orthogonality express themselves in strange ways and each dimension codes different semantic meaning.

Therefore, if the training data is highly consistent you are by definition reducing some complexity and/or encoding better similarity.

In Go the statement

    result, err := Storage.write(...)

    if err != nil { ... }

   try { Storage.write() } catch (error) { ... }

I have to steer models hard for C++. They constantly suggest std::variant :P

...for which ample training data is available.

> This makes sense, given that they are derived from text translation systems.

...for languages with ample training data available.

Yes, LLMs can combine information in novel ways. They are wonderful in many respects. But they make far more mistakes if they can't lean on copious amounts of training data. Invent a toy language, write a spec, and ask them to use it. They will, but they will have a hard time.