[little_name]

> LLM’s degree of adherence to training patterns can be directly tuned, per request (up to whatever the maximum it is capable of.)

I think people often forget the behavior of instruction following is still based on SFT training data. This is exactly "adherence to training patterns". Prompt engineering is not elixir, it's just a way to utilize the patterns seen in training data.

[dragonwriter]

> Prompt engineering is not elixir

I’m not talking about prompt engineering when I talk about per request tuning of how closely it follows established patterns, I’m talking about inference parameters (temperature, top_p, top_k, are common for most models and ways of calling them, some others may be available, too.)

[little_name]

There is a thread below discussing tuning temperature and why it's not tied to creativity. I think the point is temperature and other sampling parameters don't have any inherent mechanism to control the adherence to training pattern as a whole (all top-X candidates are all from training data). Deviating from the max probability in next token generation has no relation with thinking out of box. It does give more generation results that may be lucky to hit something, but I doubt the cost effectiveness