Comment by nowittyusername

I've done experiments and basically what I found was that LLM models are extremely sensitive to .....language. Well, duh but let me explain a bit. They will give a different quality/accuracy of answer depending on the system prompt order, language use, length, how detailed the examples are, etc... basically every variable you can think of is responsible for either improving or causing detrimental behavior in the output. And it makes sense once you really grok that LLM;s "reason and think" in tokens. They have no internal world representation. Tokens are the raw layer on which they operate. For example if you ask a bilingual human what their favorite color is, the answer will be that color regardless of what language they used to answer that question. For an LLM, that answer might change depending on the language used, because its all statistical data distribution of tokens in training that conditions the response. Anyway i don't want to make a long post here. The good news out of this is that once you have found the best way in asking questions of your model, you can consistently get accurate responses, the trick is to find the best way to communicate with that particular LLM. That's why i am hard at work on making an auto calibration system that runs through a barrage of ways in finding the best system prompts and other hyperparameters for that specific LLM. The process can be fully automated, just need to set it all up.