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Notes -
LLMs are more than token predictors, but they are mostly token predictors. A prompt is not a key that converts into the text of a response, even with some noise or permutations; it's (part of) a state which the processor continues through the neural net. Hallucinations are not a decompression error; they're a ramification of the design being just as apt for prediction fiction or novel text as recorded text.
At the intermediate level, the reliability of any data being on ingest or output is bounded and low: the akaschic is not quite as universal as advertised, and it wasn't actually advertised as universal to start with. Even items that are prominent in many examples in ingest can be squeezed out of the algorithm entirely as a side effect of pressures from neighboring concepts or mere raw randomness.
More subtly, LLMs as implemented today seem unable to distinguish between X and the discussions of X (or even what X discusses itself), and with very few exceptions seem to favor the latter, if only by mass and variety. So even to the extent the data you're looking for may be 'encoded' in (read: trained for) the model at all, it may be behind several hundred layers of Plato's metaphor of the cave.
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