After learning how to prompt clearly and iterate effectively, a natural question emerges: Does it matter which LLM I use if I’m iterating well? In the short run, the honest answer is no. If you’re clear in your intent and willing to refine direction, most modern LLMs will get you where you need to go. Prompting and iteration do a lot of the heavy lifting early on. That’s why many people experience an initial breakthrough and think, “Okay, I’ve got this.” And they do. At first. 💎 Why Iteration Levels the Field Early When you’re iterating well, you’re doing a few important things: - Clarifying what you actually want - Responding to output instead of restarting - Adjusting direction in small, intentional steps Those behaviors transfer. They work across LLMs because the interaction pattern is the same: input → response → refinement. In that phase, differences between LLMs fade into the background. You’re building skill, not dependency. 💎 When Fit Begins to Show Up As AI becomes something you use regularly—not occasionally—another shift starts to happen. You’re no longer experimenting. You’re working. And that’s when fit begins to show up. Not in dramatic ways In small ones that compound over time. You notice how an LLM responds to follow-ups. How much structure it assumes. How easily you can steer it without over-explaining. Tone and writing style are often where this becomes most obvious. Some people gravitate toward Claude because it feels more measured, structured, and editorial. Others prefer ChatGPT because it feels more conversational, adaptive, and easy to steer through quick iteration. Neither is better. They simply feel different to work with. And once AI becomes part of your daily rhythm, those differences start to matter. To be clear, this isn’t about specialty capabilities like coding, image creation, or domain-specific features. It’s about how naturally an LLM mirrors: - Your tone - Your writing style - The way you think through ideas

Once people understand that prompting is the foundation, the next realization is often harder to make: Iteration is not intuitive. Most of us are trained to start over when something isn’t right. We rewrite from scratch. We clear the page. We try again. That habit carries directly into how we work with AI. So instead of refining, we create a new prompt—often one that looks completely different—hoping the next output will feel like a fresh start. Ironically, that’s still iteration. The difference is that it’s happening implicitly, not intentionally. 💎 Why Iteration Feels Counterintuitive 💎 What feels like “starting over” is usually just a new instruction layered on top of the same idea. We change wording. We shift tone. We add detail. The output may look completely different, but the real change happened in the instruction, not in abandoning the process. Once you see this, something clicks: You don’t need to reset the conversation. You need to direct it. Iteration with AI isn’t about replacing prompts. It’s about shaping outcomes—often with fewer words, not more. 💎 The Feedback Loop That Actually Matters 💎 AI isn’t static software. It responds. That means the real value doesn’t come from a single instruction—it comes from the feedback loop: You ask. AI responds. You adjust. AI improves. That loop is where clarity forms. If a response is close but not quite right, that’s not failure—it’s information. It tells you exactly what to refine next. 💎 Small Adjustments, Big Impact 💎 Iteration often looks deceptively simple: - “That’s close—make it more concise.” - “Same structure, different audience.” - “Expand only this section.” - “Keep the idea, change the tone.” - “Apply this somewhere else.” These aren’t new prompts. They’re course corrections. Over time, those small adjustments compound into noticeably better outcomes. This is why experienced users don’t restart—they steer. 💎 Where the Diamond Gets Cut 💎 Prompting may be the diamond—but iteration is how it’s refined.

A 5-Part Series on Prompting, Iteration, and Finding Your Own AI Rhythm We talk a lot about AI tools— Models. Apps. Updates. But beneath all of it, there’s one thing that quietly connects almost everything in modern AI: 💎 Prompting. Not as a trick. Not as a hack. But as the foundation—the way we communicate intent, context, and direction to AI. 💎 Prompting — often taken for granted, yet once refined, it unlocks real AI power. Over the next few posts, I’m kicking off a 5-part series called: 💎 Prompting: The Foundation for Unlocking Real AI Power We’ll explore: - Why prompting shows up everywhere, no matter the tool - Why iteration (not perfection) is the real superpower - Why some AI tools feel intuitive while others don’t - How prompting naturally enables us to expand from simple use to workflows and systems - And why there is no single “right” path when learning AI This series will reveal how such a simple act can unlock so much real capability. For the complete Series articles, visit: Series Hub ✨ AI Bits & Pieces — helping people and businesses adopt AI with confidence. Image created using “prompts” with ChatGPT.

There’s a lot of discussion about how overwhelming AI can feel—especially with the sheer breadth of products and services, and the speed at which new revisions and updates keep rolling out. For many people, it creates a constant sense of playing catch-up. So whether you’re just starting out, or you feel like you’re simply keeping pace, the best place to start—or recenter—is prompting. Prompting is the foundation of working with AI. It’s the way we express intent, provide context, and guide direction when interacting with intelligent systems. Not as a trick. Not as a hack. But as the underlying mechanism that determines whether AI feels helpful—or frustrating. 💎 Why Prompting Comes First 💎 Every AI interaction follows the same basic loop: You give input. AI responds. You react, refine, or redirect. No matter the tool, that loop doesn’t change. If your intent is unclear, the output will be too. If your context is thin, the response will be shallow. If your direction is vague, results will feel inconsistent. Better tools don’t fix that. Clear prompting does. 💎 Prompting Is About Thinking, Not Typing 💎 It’s easy to think prompting is about what words you use. It’s not. It’s about: - Knowing what you’re actually trying to achieve - Providing enough context for AI to work intelligently - Setting boundaries and expectations - Being willing to refine instead of restarting The strongest prompts usually come from clearer thinking—not longer instructions. 💎 Why This Transfers Across Tools 💎 This is why prompting shows up everywhere. Once you learn how to: - Frame a request clearly - Ask follow-up questions - Adjust direction through iteration You’ll notice something interesting happen. New AI tools start to feel familiar. Different interfaces. Different outputs. Same underlying conversation. That’s not coincidence. That’s the foundation at work. 💎 The Diamond in the Rough 💎 Prompting is often taken for granted. Because it feels simple, people assume it’s basic.

If benchmark charts make your eyes glaze over, you’re in the right place. Most people don’t need all the benchmarks — just the ones that actually change how an AI model feels when you use it. That’s why we’re launching a brand-new classroom this week: The Big 5 Benchmarks — Explained for Beginners. No jargon. No math. No research papers. Just simple, practical explanations of the five benchmarks that matter most when choosing or comparing AI models. Inside, you’ll learn what each benchmark really measures — each one includes a plain-English breakdown and real-world examples so you immediately see why it matters. Because when you know the right benchmarks, you stop guessing which model is better — and start choosing the model that fits the job. 💡 Takeaway Understanding the Big 5 benchmarks turns AI from mysterious to manageable — and helps you use the right model with confidence. Go to Classroom: Get Smarter LLMs Benchmark Stats Made Easy https://www.skool.com/ai-bits-and-pieces/classroom/fd2b30f7?md=79d6fd21074e45c5a88c54bf2389d67a