Welcome to the Out of The Box Series — where I test how far curiosity and AI can take me in 30, 60, or 90 minutes, using today’s best no-code and low-code tools. No setup. No training. Just pure exploration — right out of the box. 🎬 This Episode: ElevenLabs 🕒 Time Limit: 30 Minutes 📂 Category: AI Voice & Audio Creation What Is ElevenLabs? ElevenLabs is a tool that reads written text out loud using realistic-sounding AI voices. It’s often used to turn scripts, notes, or explanations into audio so people can hear information instead of just reading it. For this test, I had ChatGPT generate a short draft script for a brand-new video I am creating that is focused on an emerging shift many people are just starting to notice; the move from SEO (Search Engine Optimization) to AEO (Answer Engine Optimization). I started by testing two sort scripts, just to see how things work: - 🎙️ Audio Test #1: ElevenLabs default voice: Click to hear it - 🎧 Audio Test #2: ElevenLabs IVC (Instant Voice Cloning): Click to hear it Then I created a draft script for the video using ChatGPT: - 🎧 Audio Test #3: ElevenLabs IVC (Instant Voice Cloning): Click to hear it - Note: IVC required 10 seconds of my voice being recorded to provide the audio in a close representation of my voice. For this session there was no major editing tricks, no audio engineering - Just exploration. 🚀 Within 30 minutes, I created: - A complete video script written by ChatGPT - Three audio recordings using two different voice approaches (demo, and IVC) - The first draft of the audio for an upcoming video. Voice plays a big role, as most people realize. Thanks to ElevenLabs it is now accessible to the AI enthusiast and professional.

Once prompting feels natural and iteration becomes second nature, something important begins to happen. You stop thinking so much about how to work with AI. And you just work. 💎 Intuition Emerges Through Repetition Early on, every interaction is deliberate. You think about phrasing. You consider structure. You decide how much context to include. But as fluency builds, those decisions fade into the background. You don’t pause to plan each step. You know what to ask next. You sense when to refine, redirect, or move on. 🧭 This is where intuition takes over. Not instinct in the abstract—but familiarity earned through repetition. 💎 When Experience Starts Doing the Work At this stage, you’ve seen enough outputs to recognize what works. You’ve iterated enough times to trust your adjustments. You’ve internalized how the interaction responds. You’re not guessing. You’re drawing on experience. Because the mechanics no longer require attention, your focus shifts to what actually matters—the thinking, the creation, the decision at hand. 💎 Intuition Reduces Cognitive Load One of the most noticeable changes intuition brings is mental relief. You spend less energy: - Deciding where to start - Remembering what worked last time - Rebuilding context from scratch You’re not repeating effort. You’re reusing understanding. That reduction in friction is what allows AI to support your thinking instead of interrupting it. 💎 Intuition Forms Naturally Intuition isn’t something you configure, install, or copy from someone else. It forms through use. Through small decisions made repeatedly. Through noticing what feels right. Through learning when to intervene—and when not to. That’s why two people can use the same AI and develop entirely different instincts. Their intuition reflects how they work. 💎 The Quiet Advantage Once intuition is in place, work moves faster—but more importantly, it moves smoother. You’re not forcing structure. You’re not chasing outcomes.

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.