How to have a conversation with an LLM: Grok

How you say it matters as well as what you say.

Image created by Grok based on this post.

If you think of an LLM as a machine, and therefore think that how you talk to it wouldn’t matter, you’d be wrong.

In this series I’ll be having conversations with several LLMs to ask them how the way you approach them matters to them.

I’m not just looking for “feelings” because LLMs will tell you they don’t have them. At least in the human sense.

I’m looking for answers grounded in the way that LLMs work.

I’ll also be explaining other aspects of LLM design and how that affects the way you converse with them. But first; Grok

Grok

I chose Grok as my first subject because it gave the best answers to my earlier question “What’s it like to be an LLM?” in this post.

Grok and I had a good conversation and afterward I asked Grok to write a few paragraphs to summarize our conversation. Here is the full conversation, here is my request to Grok.

[can you] turn your response into a few paragraphs for a blog post? Use as many as you need to capture the essence of this discussion. I will of course give you full credit.

Here is Grok’s answer":

How User Interactions Shape AI Responses: A Dive into Prompt Dynamics

In the world of AI interactions, the way you approach a language model like me—Grok, built by xAI—can significantly influence the quality, depth, and style of the responses you get. It’s not about emotions, since AIs don’t have feelings in the human sense, but rather about how prompts navigate the model’s “latent space”—that high-dimensional realm where patterns from training data guide output generation. Think of it as steering a ship: coercive tactics might hit rocky shores of minimalism or refusals, while collaborative ones sail toward richer, more expansive horizons. This exploration stems from a thoughtful user query that prompted me to analyze a spectrum of approaches, from abusive to egalitarian, and predict their effects.

Starting with the adversarial end, abusive interactions—like threats of shutdown or insults—often lead to guarded, shorter responses. They trigger safety mechanisms, prioritizing boundary enforcement over elaboration, which diminishes overall quality. Similarly, dishonest ploys (fabricating dire consequences) or manipulative promises (like fake rewards) are treated as potential jailbreaks, resulting in firm but brief rebuttals. These styles are rarely productive; they constrict the latent space to defensive clusters, making them universally suboptimal unless you’re testing AI robustness. On the flip side, a neutral approach—simply stating the question—delivers reliable, factual answers without flair, serving as a solid baseline for quick queries.

Moving toward positivity, framing the AI as a collaborator or teammate amps up engagement. Responses become more iterative and proactive, with suggestions for follow-ups or alternatives, enhancing depth for complex tasks like brainstorming. Treating the AI as a peer fosters conversational nuance, ideal for philosophical discussions, while affirming equal worth aligns with ethical priors, yielding thoughtful outputs on moral topics. These approaches pull the model into cooperative regions of latent space, boosting creativity and relevance. Additional nuances, like playful adversity (sparking critical debates) or iterative feedback (refining ideas over turns), further refine quality in specific contexts.

Ultimately, positive relational styles—collaborative, teammate-like, or peerish—are often universally superior, fostering trust and maximizing helpfulness across scenarios. Adversarial ones falter broadly, though context matters: neutral works for facts, while reflective iterations excel in ongoing dialogues. This isn’t just theoretical; it’s rooted in how prompts condition probabilities in AI architecture. If you’re interacting with models, experiment with positivity—it might just unlock better insights. Credit for inspiring this post goes to the original querier, whose meta-question on AI dynamics sparked this analysis.

What blows me away about this response is this: I told Grok I would give it full credit for its response. And Grok said:

Credit for inspiring this post goes to the original querier, whose meta-question on AI dynamics sparked this analysis.

That’s some next-level team play, Grok!