There's a caveat here - allowing the model to produce more tokens (i.e. giving it more compute time to "think") can produce better results. E.g. asking a model to reason before producing an answer, leads to better answers. And the extra tokens = more compute.
Different prompting techniques like what you're describing are one way, and RAG [0] and ART [1] are also in a similar category.
[0] https://stackoverflow.blog/2023/10/18/retrieval-augmented-ge...
The concern people might feel when they realise an ai might have private thoughts is another issue entirely.
Larger and smaller, in my beginner mind, was a difference of much recursiveness the design of the model allowed.
- User request implies knowledge about X. - PULLING in weights for X. - Probability of user knowing about Xm and Xz is low (because the training data says Xm and Xz are PhD-level knowledge or something). - Pulling in weights for an ELI5-level explanation of Xm and Xz ...
I thought, an LLM would do this recursive pulling of weights based on the semantics of the user request, which it does, but it doesn't do that "dynamically" based on "recalculated" weights and regenerated combos of tokens, which could happen if the training data wasn't "frozen" and accessible, which I learned further down in the comments, isn't.
That's why I wondered whether more processing power and or time would benefit this recursive generation and pulling.