you try to not give it choices implied in your question that it can cling to. Ask a question as neutrally as you can. The worst you can do is "isn't it true that...".

That said, system prompts can empower the llm to actually challenge and contradict you. How well it is using which method and data for it, is probably down to the model .
Now THAT said... getting medical advise from an AI beyond the basics AND relying on it is madness.

(without internet access or specific training data it's knowledge will likely be inferior to yours, and if the system prompt also stops it from speculating, it just takes your word for it even more).

Hmmm... i'd prefer a factual answer over personal bias; At least when factual answers are possible.

But for writing and ERP and the like, i can see this being interesting to know how we affect the models. Understanding something is the best way to fine-tune them.

This is only a logically and logistically correct assessment IF the assumption is based on curated data related to the very capabilities which your "mirror" requires to amplify those biases. The alternative is the LLM simply echoes reflective similarity directly associated with NEARBY echoed words, rather than logically related context and content. Instruct helps a lot, so does harmony, and the alternative forms of them - but the BIAS STILL FORMS.
If your bias is something that LLM does not have the relational associations to internal data nor has ever been taught the capability to deduct the logistical responsiveness from those biases, you are likely reflecting your personality quirks and biases onto a machine that simply cannot reflect them and thus the machine will simply... begin to echo those back to you.
This is a common self reflective bias that many of my introspective analysis and self analytical conversations defaulted to when assessing complex logistical and introspective analysis of large structures. This is most commonly amplified and incorrectly confident while discussing those problems with a single large LLM.
Communicate those same unfiltered conversational pieces to another large LLM and you will most definitely find different mirrored effects and different biases. You'll often find GROK, Gemini, and Claude all returns different responses to those same assessments.
Now... if all four say yes, the math lines up, the stars align, and the systems can in fact work if X and Y and Z - you might have a potential solution. EVEN THEN it will be a damn journey to make it work.
LLMs are often VERY WRONG even as a collective when it comes to large data in association with intricate complex technical work. Sometimes it's a single incorrect assessment from a random book that was fed in 500 times from a single topic that was simply disproven at some point, and yet there are still direct biases associated with those incorrect concepts. This amplifies the further you dive down the rabbit hole and is easy to confuse the llm, trick the llm with input, and even easier to break the llm's entire pattern with them because you're already so deep down the rabbit hole that you're accessing heavy noise.

Exactly! LLMs are basically mirrors for the way you ask questions. I’ve noticed that the way you frame a prompt completely shapes the response: vague or negative prompts give answers that feel way harsher or more extreme than reality. These models aren’t handing out objective truth; they’re reflecting and amplifying the assumptions baked into your prompt. Even small tweaks in wording can flip the tone or angle of the answer completely, which is wild once you start paying attention.