[Metricon]

The best way to think of prompts are as somewhat equivalent to search engine queries. Perhaps the most fascinating part of these transform models are how they resemble the process of memory recall and association.

In a 4GB space is embedded potentially millions of images to tokens which correlate with language descriptions. So indicating the desired result somewhat simulates this process of recall and association.

The process lacks any internal emotional motivation to construct any "desired" outcome that a human would, so it needs both a "seed" number and description of the desired recall elements to achieve a specific result.