Feedback for “LLM Settings”

> The general recommendation is to alter one, not both.

I noticed in the explanation that the general recommendation is to alter either `temperature` or `top_p`, but not both. However, to me, it seems to make sense to use both:

- If `temperature` is set to 0, it would seem that `top_p` has no effect, as the output will always be deterministic, picking the most likely next token.
- Conversely, if `temperature` is set to 1, allowing for more creativity, but `top_p` is set to a low value like 0.01, it seems that the creativity could be effectively restricted to only the top 1% of probable words.

Wouldn't it then be practical to use both parameters together? `Temperature` could control the overall creativity of the model, while `top_p` sets a boundary on the word options for the next token. This could allow for a more nuanced control of the model's output, fitting various use cases. Could you clarify if this approach is indeed viable, or if there are potential drawbacks or complexities I'm overlooking?