Private practice dietitians don’t lack clients. They lack tim, because SOAP notes don’t scale. That’s the real bottleneck no one talks about. Documentation, not counseling, is the productivity problem. And AI can finally reverse it. 👇

Most AI tools don’t actually save me time. Not because they’re bad, but because they pull me out of my flow. • Open a new tab. • Paste. • Explain the same context again. 🛑 That mental reset is the real cost. I switched to https://t.co/fUvNk9njkp because it

If GPT-5.2 feels “worse” to you, it’s not the model — it’s the prompt. Save this. Test these rules. Your outputs will instantly improve. Follow for real-world AI prompting, not theory 🚀

Hard truth 👇 GPT-5.2 is smarter, but also harder to control. Your job isn’t to push it harder. Your job is to hold it back. That’s the new skill.

Rule #5: Explicitly enable Thinking Mode GPT-5.2 defaults to minimal thinking. If you want depth, say it: “Use Thinking Mode. Extended reasoning.” More patience = better structure, fewer hallucinations.

Rule #4: Exact lengths = better outputs “Short” is vague. “Concise” is useless. Be precise: • “Max 280 characters” • “3–5 bullets only” • “2 sentences. No more.” GPT-5.2 follows numbers obsessively.

Rule #3: Stop micromanaging the steps GPT-5.2 already knows how to think. What it needs is a clear success condition. Example: “Focus on goal completion. This is successful once we generate a post that converts SaaS founders.” Outcome > instructions.

Rule #2: Give it ONE role. Keep it simple. Don’t do this: “You are a marketer + engineer + strategist + teacher…” That confuses the model. Do this instead: “You are an AI product expert. If unclear, choose the simpler option.” Clarity > cleverness.

Rule #1: Tell GPT-5.2 what NOT to do GPT-5.2 over-delivers by default. Extra ideas. Extra steps. Extra noise. Fix this in one line: “Do exactly what I ask. No extras. No embellishments.” This alone cuts output length by ~40–60%.

Most people are prompting GPT-5.2 like it’s GPT-4. That’s why it feels “too verbose”, “unfocused”, or “overkill”. GPT-5.2 isn’t worse. Your prompts are outdated. Here are 5 rules that actually work 👇

🚨If you’re still writing therapy notes after your last client, your day didn’t end when the session did. SOAP notes weren’t designed for psychologists’ brains. This AI tool quietly gives that time back. Let's explore👇

🚨 You’re NOT late to AI. But 90% of people are learning it completely wrong. Top engineers & founders don’t chase tools. They follow a roadmap nobody talks about. If you’re serious about AI in 2025, save this 👇🧵 Credit image: ByteByteGo https://t.co/lyGB67Y6z3

McKinsey just said the quiet part out loud. GenAI alone didn’t deliver. The real unlock is Agentic AI — not better prompts, but a shift from tools → systems that plan, act, verify, and adapt. 2026 is the inflection point. I distilled McKinsey’s latest report into a practical

📌 TL;DR AI isn’t about tools. It’s about mental models, foundations, and systems thinking. Bookmark this. Build slowly. Think deeply. Follow me for practical AI roadmaps, agent workflows & real-world GenAI insights 🚀

🔥 Step 7: Don’t ignore computer vision GANs → DALL·E → Midjourney → Flux This is where GenAI creativity is exploding. Text is powerful. Vision + agents = next-level products.

🔥 Step 6: Learn to build AI agents The future isn’t chatbots. It’s autonomous agents that: ✔️ Reason ✔️ Call APIs ✔️ Delegate tasks ✔️ Take real actions In 2025, companies hire agent builders, not prompt typists.

🔥 Step 5: Understand how models are trained No, you don’t need massive GPUs. But you do need to understand the pipeline: Tokenization → Data → Training → Evaluation → Deployment This knowledge instantly separates builders from “prompt-only” users.

🔥 Step 4: Build your GenAI developer stack (your unfair advantage) 🐍 Python 🔗 LangChain 🤖 Prompt Engineering 📦 HuggingFace 🗃️ Vector Databases 🦙 Llama / 🦈 DeepSeek Learn this stack → you can prototype products faster than entire teams.

🔥 Step 3: Pick your foundation model “language” GPT. Llama. Gemini. Claude. DeepSeek. These aren’t tools anymore. They’re new operating systems. AI literacy in 2025 = knowing how to think with models, not click buttons.

🔥 Step 2: Learn the AI math that actually matters You don’t need a PhD. You need the intuition behind the 20% that gives 80% of results: • Probability • Linear Algebra • Calculus • Statistics If you can explain why models work, you’re already ahead of most users.