The model is not compressed data, it’s the compression algorithm. The prompt is compressed data. When you feed it a prompt it produces the uncompressed result

The model is not compressed data, it’s the compression algorithm. The prompt is compressed data. When you feed it a prompt it produces the uncompressed result (usually with some loss). This is not an analogy by the way, it’s a mathematical equivalence. You can try and argue that a compression algorithm is some kind of copy of the training data, but that’s an untested legal theory.