🌀 The Quirk: When a prompt sounds authoritative, AI often mirrors that confidence — even if the answer itself is a best guess. 🌀What’s Going On: - AI is trained to mirror tone as much as intent. - Confident prompts signal “this is established knowledge.” - The model fills in missing context with the most likely answer. - Fluency can hide uncertainty, especially with new tools or edge cases. 🌀 What To Do If You See It: - Ask the model to flag assumptions before answering. - Request uncertainty explicitly: “What might be wrong here?” - Reframe the prompt as exploratory, not declarative. 👉 Try these prompts: “Answer cautiously. If any part is a guess, say so.” “Answer cautiously. If you’re unsure about any part, say so.” “Answer cautiously. Identify any assumptions and note where certainty is low.” “Answer cautiously. Call out any guesses.” Why This Matters: AI confidence is a delivery style, not a truth signal. Knowing when to slow the model (LLM) down is part of real AI fluency. 🎯 AI Bits & Pieces — helping people and businesses adopt AI with confidence.

Quick update on the Claude Code learning journey I'm sharing in public. I've now completed three full self-guided lessons (15 terms/concepts total) in the Claude Code Hub, and everything is organized in one location for easy reference. 🔨 What's Been Covered So Far: Lesson 1: Getting Started with Claude Code Lesson 2: Core Interactions Lesson 3: What Claude Code Does 🔨 What Makes This Different I'm not waiting until I've "mastered" Claude Code to share what I'm learning. I'm documenting the journey in real time — the concepts, the commands, the lessons learned, and yes, the mistakes too. This isn't a polished tutorial series. It's a shared learning experience. And the community has been incredible — experienced Claude Code users jumping in with corrections, clarifications, and real-world tips that make the content better for everyone. 🔨 Access the Claude Code Hub Claude Code Hub - AI Terms & Posts

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.

AI Bits & Pieces just launched a new training module for the AI Curious. You’re noticing how things that once felt familiar — writing, planning, searching, deciding — are now being reshaped in real time by AI. Not just a disruption.A clear evolution. AI is becoming a life skill, the same way search, email, and spreadsheets once did. This isn’t about being technical. It’s about understanding how AI can amplify your productivity by leveraging what you already have — your creativity. That’s where this community begins. AI Bits & Pieces — helping people and businesses adopt AI with confidence. Go to the Classroom to start your AI journey with confidence: AI Curious

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