One of the quiet myths around AI adoption is that success comes from staying firmly in control. That if we just give the right instructions, apply enough structure, and reduce uncertainty, AI will behave exactly as we want. In reality, the opposite is often true. The biggest breakthroughs with AI tend to happen not when we tighten control, but when we learn how to collaborate. ------------- Context: Why Control Feels So Important ------------- Most of us were trained in environments where competence was measured by precision. Clear plans, predictable outputs, and repeatable processes were signs of professionalism. Control was not just a preference, it was part of our identity. If we could define every step and anticipate every outcome, we were doing our job well. AI disrupts this deeply ingrained model. It does not behave like traditional software. It responds probabilistically, offers interpretations rather than guarantees, and sometimes produces outputs that are surprising, imperfect, or simply different than expected. For many people, this creates discomfort before it creates value. That discomfort often shows up as over-structuring. We try to lock AI into rigid instructions. We aim for the perfect prompt. We narrow the interaction so tightly that there is no room for exploration. On the surface, this looks like responsible use. Underneath, it is often an attempt to preserve a sense of control in unfamiliar territory. The challenge is that excessive control quietly limits what AI can contribute. It turns a potentially collaborative system into a transactional one. We ask, it answers, and the interaction ends. What we lose in that exchange is insight, perspective, and the chance to think differently than we would on our own. ------------- Insight 1: Control Is Often a Comfort Strategy ------------- When we encounter uncertainty, control feels stabilizing. It gives us the sense that we are managing risk and protecting quality. With AI, this instinct is understandable. We worry about errors, misalignment, or appearing unskilled if the output is not perfect.

I have to share a success with you great people. The few but well-chosen contents of the speakers activated my nerd brain and gave me an idea. I have developed a product within 10 hours of work over the last 4 days. PRECISE PROMPTS, always questioning, 3 PLAYBOOKS created ... tadaa a product which I am already testing. The automatic link between AI and data storage is still missing, but I'll manage that too I would like to ENCOURAGE you to try it out and use the MOMENTUM of this supportive, positive community. Thank you community and @AI Advantage Team

If you're still writing prompts the length of "War and Peace," and ChatGPT still produces some kind of nonsense, this article will change your life. Today I'll tell you about a programming method that makes neural networks much more responsive. A Prompting Method That Changes Everything - JSON Prompts and How to Use Them By the way, I'm Anton Shadrin. My job is to implement neural networks in business and make AI work for you, not you for it. What is this JSON beast and why is it better than regular prompts? JSON is simply a way to structure information. Imagine that instead of a solid sheet of text, you give ChatGPT a clear list: this is the role, this is the task, and this is the response format. A typical prompt looks like this: You're an experienced copywriter. Write a post for Instagram about the benefits of meditation. The text should be friendly and motivating, 150-200 words long. Use emojis and add a call to action at the end. And here's the same prompt in JSON: { "role": "Experienced social media copywriter", "task": "Write a post for Instagram about the benefits of meditation", "style": "Friendly and motivating", "length": "150-200 words", "format": "Text with emojis", "cta": "Call to start meditating today"} See the difference? In the second case, the neural network understands exactly what is being asked of it. No fluff, no double interpretations—a pure structure. Why this works better (spoiler: it's in the neural network's brain) ChatGPT was trained on millions of lines of code, where JSON is a standard data format. It's like its native language. When you feed information in JSON, the neural network switches to "oh, this is serious" mode and starts working more accurately. I tested both approaches on over 50 tasks. The result: JSON prompts produce the desired result on the first try 80% of the time, compared to 60% with standard ones. The time savings are enormous. Step-by-step instructions: creating your first JSON prompt Step 1. Define the structure

Most people are stuck with generic superficial answers talking to ChatGPT. But if you follow this simple 3-step process, your answers will be 10x better and personalized to your situation. Step 1: Start with your primary question: > "How do I run ads?" Step 2: Tell ChatGPT to assume an expert role: "Imagine you’re a top 0.1% ads expert (such as Dean Graziosi). How do I run ads?" Step 3: Tell ChatGPT to ask you questions so that its answers will be ultra personalized for you: "Imagine you’re a top 0.1% ads expert (such as Dean Graziosi). How do I run ads? Ask me clarifying questions until you’re 95% confident you can complete this task successfully."

Stacking , my heart was the best exercise ever Done first time Doing this exercise made me feel so good