Common ChatGPT Usage Mistakes

I spent 18 months breaking ChatGPT 100 different ways. Vague prompts. Bad tone. Zero traction. But after all that trial and error, I found a formula. Here are 7 ChatGPT hacks that unlocked real leverage & saved me hundreds of hours. I won't waste your time: 1. Use it like a strategist, not a search engine. Stop asking it for facts. Ask it for angles, systems, frameworks. You want leverage, not just answers. Instead of “what are good business ideas?” Try: “What are 7 business ideas people are quietly discussing in subreddits that are likely to explode in 3 years?” Huge difference. 2. Feed it (A LOT OF) real context, not vague prompts. “Make this shorter” is weak. “Make this understandable to a 5th grader” is strong. Upload reference docs, paste examples, show what you actually want. It’s not magic. It’s math + context. 3. Build workflows, not one-off prompts. You don’t want a one-time email. You want a system that writes great emails every time. Build a custom GPT and feed it your best outputs & ask it what makes them great. Now you’ve got an engine, not just a button. 4. Layer prompts like a builder, not a browser. Don’t expect gold from your first input. Ask. Refine. Reword. Reframe. Push. Test. It's clay, not a vending machine. Massage it! 5. Use it to A/B test content Got 8 possible headline titles? Ask it to write them and post 4 at a time on IG as a poll. Winner advances. Run a bracket. By the end, you’re not guessing what people click, you know. 6. Reverse-engineer your favorite writers. Paste in writing you love and ask: “What makes this writing great?” Then say: “Write like this from now on.” You just hired your favorite author… for free. 7. Ask it to optimize your own life. Upload your bank statement and say: “Where am I wasting money? What can I cancel? What can I replace with a cheaper service?” Give it receipts, transcripts, screenshots and let it point out what you’re blind to. The people who get the most out of ChatGPT aren’t the smartest. They’re just the most specific. Garbage in, garbage out. Context in, leverage out. Be strategic. Get weird. Iterate forever. You’ll be 10x faster than everyone else. Follow me Chris Koerner if you found this interesting!

Most people are using ChatGPT wrong ❌ They treat it like a search engine ("Write a post about chairs"). Then they wonder why the output is generic, hallucinated, or boring. To get the top 1% of results, you need to stop asking questions and start writing specifications. I use a 5-step framework called the Universal Prompting Toolkit: 1️⃣ Role (Who acts?) 2️⃣ Context (The background) 3️⃣ Constraints ( The guardrails) 4️⃣ Format (How it looks) 5️⃣ Examples (The style guide) Here is exactly how to combine all 5 into a "Super Prompt" (Steal this template): 👇 "Act as a senior marketing copywriter to create a product description for our new 'Zenith' ergonomic office chair, specifically targeting remote workers suffering from back pain [Role & Goal]. The core feature to highlight is 'dynamic lumbar support' that automatically adjusts to the user's posture [Context]. Please strictly adhere to a 50-word limit, maintain an empathetic and professional tone, and avoid overused buzzwords like 'game-changer' or 'revolutionary' [Constraints]. Format your response as a JSON object with keys for 'Headline' and 'Body_Text' [Output Format]. As a reference for the desired style and format, if the product were a noise-canceling headset, the output would be: {'Headline': 'Focus in Silence', 'Body_Text': 'Block out the chaos of home life with active noise cancellation designed for deep work.'} [Example]" Why this works: Instead of a generic paragraph, the AI now knows exactly who to be, what to avoid, and exactly how to structure the data for my code. Follow Burhan Sebin for more practical guides like this.

47 profiles. 8 hours. 3 of them weren't on LinkedIn. That was last Tuesday's brief at First2 Group. The hiring manager said "impossible to fill." I pasted 𝐨𝐧𝐞 𝐩𝐫𝐨𝐦𝐩𝐭 into ChatGPT and it outperformed 2 hours of manual LinkedIn Recruiter searching. One prompt. I didn't refine the output on the first 3 briefs. Took the AI results at face value. Cost me a week of wasted outreach and a client who stopped returning calls. (Most recruiters are still pasting job descriptions into ChatGPT and wondering why the results are useless.) This is the prompt I now run for 𝐞𝐯𝐞𝐫𝐲 𝐧𝐢𝐜𝐡𝐞 𝐭𝐞𝐜𝐡 𝐬𝐞𝐚𝐫𝐜𝐡: "Act as a senior technical recruiter. Identify candidates for a [Job Title] role with 5+ years in [Key Skill 1] and [Key Skill 2]. Focus on candidates who've contributed to open-source projects or published technical work. Exclude anyone with only consulting experience. Provide 10 profiles with a 2-sentence summary and a personalised opening line for outreach." The difference between this and "find me Python developers" is 𝟒𝟕 𝐜𝐚𝐧𝐝𝐢𝐝𝐚𝐭𝐞𝐬 𝐯𝐬 𝟑𝟎𝟎 𝐢𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐩𝐫𝐨𝐟𝐢𝐥𝐞𝐬. Here's what I actually think: 90% of recruiter prompts fail because they skip three things — exclusion criteria, output format, and a specific persona. Add those three and the output transforms. Most people think the problem is the AI. After deploying prompts across 28 businesses, I think the problem is that we treat ChatGPT like a search bar instead of a colleague who needs a proper brief. Prompt engineering didn't make sourcing faster. It made lazy sourcing obvious. Are you building role-specific prompt templates or still copy-pasting generic ones? Save this prompt before your next niche search.

Stop blaming ChatGPT, Claude , or Grok for bad outputs when you're using it wrong. Here's the brutal truth: 90% of people fail at AI because they confuse prompt engineering with context engineering. They're different skills. And mixing them up kills your results. The confusion is real: People write perfect prompts but get terrible outputs. Then blame the AI. Plot twist: Your prompt was fine. Your context was garbage. Here's the breakdown: PROMPT ENGINEERING = The Ask CONTEXT ENGINEERING = The Setup Simple example: ❌ Bad Context + Good Prompt: "Write a professional email to increase our Q4 sales by 15% targeting enterprise clients with personalized messaging and clear CTAs." AI gives generic corporate fluff because it has zero context about your business. ✅ Good Context + Good Prompt: "You're our sales director. We're a SaaS company selling project management tools. Our Q4 goal is 15% growth. Our main competitors are Monday.com and Asana. Our ideal clients are 50-500 employee companies struggling with team coordination. Previous successful emails mentioned time-saving benefits and included customer success metrics. Now write a professional email to increase our Q4 sales by 15% targeting enterprise clients with personalized messaging and clear CTAs." Same prompt. Different universe of output quality. Why people get this wrong: They treat AI like Google search. Fire off questions. Expect magic. But AI isn't a search engine. It's a conversation partner that needs background. The pattern:  • Set context ONCE at conversation start • Engineer prompts for each specific task  • Build on previous context throughout the chat Context Engineering mistakes:  • Starting fresh every conversation  • No industry/role background provided  • Missing company/project details • Zero examples of desired output Prompt Engineering mistakes:  • Vague requests: "Make this better" • No format specifications  • Missing success criteria • No tone/style guidance The game-changer: Master both. Context sets the stage. Prompts direct the performance. Quick test: If you're explaining your business/situation in every single prompt, you're doing context engineering wrong. If your outputs feel generic despite detailed requests, you're doing prompt engineering wrong. Bottom line: Stop blaming the AI. Start mastering the inputs. Great context + great prompts = consistently great outputs. The AI was never the problem. Your approach was. #AI #PromptEngineering #ContextEngineering #ChatGPT #Claude #Productivity #AIStrategy Which one have you been missing? Context or prompts? Share your biggest AI struggle below.

Stop asking ChatGPT to “sound like a human.” That prompt is vague. Vague prompt in, weak output out. If you want human answers, ask for human context. Use prompts that force perspective, experience, emotion, and judgment. Below are prompts that actually work. Keep them as they are. Just insert your question. 1. Human Expertise Mode “Answer my question like a real human expert with 10+ years of real-life experience in this field. Avoid textbook explanations. Give lived-experience insights, mistakes, human nuance, and practical examples. My question: [insert question].” 2. Emotionally-Aware Human Response “I want you to answer this like a human who deeply understands emotions, struggle, motivation, and context. Speak naturally, explain your reasoning, and respond in a supportive and relatable tone. My question: [insert question].” 3. Real-Life Experience Simulation “Pretend you’ve lived through this situation personally, learned from it, and now you’re sharing wisdom with me as a friend who cares. Give honest, grounded, human advice not robotic theory. My question: [insert question].” 4. First-Person Human Perspective “Explain the answer to my question using a first-person point of view, as if you’re a real human sharing your personal experience, opinions, and insights. Avoid generic advice. My question: [insert question]” 5. Human-Like Story Explanation “Answer my question by telling a short, relatable human story that connects emotionally, teaches the lesson clearly, and feels natural not AI-generated. Then give the takeaway in simple terms. My question: [insert question].” 6. Relatable Friend Mode “Answer my question like a smart, grounded friend who genuinely wants to help. Keep the tone conversational, honest, and practical. Avoid formal or robotic wording. My question: [insert question].” 7. Deep Human Reasoning Mode “Respond to my question as if you're a thoughtful human reflecting on life. Show reasoning, doubt, clarity, and perspective. Break the answer into natural thinking steps before concluding. My question: [insert question].” ----- The mistake people keep making is asking for “human tone” instead of human thinking. Tone is surface level. Perspective is everything. If you want better answers, stop chasing polish and start demanding context, experience, and judgment. That’s where the magic actually lives. For more updates like this: 1. Scroll to the top 2. Click "View my newsletter" 3. Subscribe, and you'll never miss a thing in the world of AI ever again.

99% of Developers are using ChatGPT wrong 🚨 Here’s how to be the 1% who level up. Most devs open ChatGPT when they: 👉 Get stuck on a bug 👉 Need boilerplate code 👉 Want a quick explanation of an error That’s fine. But it’s surface-level use. You’re treating ChatGPT like Stack Overflow on steroids. And that’s exactly why you’re not getting ahead. 💡 The 1% use it differently: They don’t just ask for answers. They use it to amplify their thinking, not replace it. Here’s how they do it 👇 1️⃣ System Design Brainstorming Instead of “write me an API,” ask: "Compare the trade-offs between designing this API with GraphQL vs REST. Which scales better for millions of users?" You’re no longer getting code. You’re getting architecture insights. 2️⃣ Learning New Tech Like a Mentor Is Sitting Beside You Don’t just say: “Explain Kafka.” Instead: "Explain Kafka as if I already know RabbitMQ, and highlight the differences in scalability and latency trade-offs." The answer is instantly contextualized to you. 3️⃣ Debugging at Scale Don’t paste errors blindly. Walk GPT through the context: "Here’s the system architecture. Here’s the error. What are 3 possible root causes at this scale?" You’re forcing it to think like an architect, not just a compiler. 4️⃣ Improve Communication Skills Strong devs don’t just code, they communicate. Use GPT to practice writing better PR reviews, technical docs, and even explaining trade-offs to non-technical stakeholders. That’s senior-level growth most devs ignore. 5️⃣ Build Mental Models Don’t accept the first answer. Always ask: “Why?” “What are the trade-offs?” “Explain it in 3 different ways.” You’ll start learning principles, not just syntax. ⚡ The Harsh Truth: Most devs are letting ChatGPT do the thinking for them. The top 1% are using it to sharpen their own thinking. AI won’t replace developers. But it will replace the ones who stop thinking critically. 💬 How are you using ChatGPT in your daily workflow? Do you think you’re in the 1%?

Most people aren’t bad at prompting ChatGPT. They’re bad at deciding what they want. This framework highlights something most AI advice avoids: Prompting isn’t about smarter wording. It’s about clearer judgment. If you want consistently better outputs, start here 👇 → Stop using ChatGPT like a search engine It performs best when you treat it like a junior collaborator. Ambiguity in. Ambiguity out. → Decide what “success” means before you prompt Format. Length. Audience. What should change after this is read? If that’s unclear to you, it will be unclear in the output. → Use real examples, not long explanations One concrete reference beats ten abstract instructions. Show the bar. Don’t describe it. → Ask for critique, not endless rewrites Have it surface weak logic, missing pieces, or where interest drops. That’s where quality actually improves. → Correct quickly and precisely “Fix this” doesn’t help. “This misses the point because X. Adjust it to Y” does. → Reset sooner than you think After too many iterations, quality degrades. Start fresh. Paste the best version. Move forward. Good prompting isn’t a trick. It’s decision-making, made visible. ↗ Repost if this helps you think more clearly about using AI ➕ Follow Gabriel Millien for practical, no-hype insights on turning AI into real work image credit: Ruben Hassid

Using AI wrong is now more dangerous than not using it at all. That should terrify you. Most early stage founders think the risk is moving too slow with AI. The real risk is moving fast in the wrong direction. Here are the 3 ways AI fails founders. And exactly how to fix each one. Failure 1: You treat AI like an oracle You feed it data. It gives you a confident answer. You trust it. Christine did this with her hair care brand. Fed ChatGPT all her customer data. Got beautiful, logical recommendations. Built everything out. Every asset bombed. AI recognizes patterns, but it has no human taste. It will give you reasoning that sounds airtight and lands completely flat with real people. Never let AI make the final call. Use it to generate options. You make the judgment. Prompt: "Give me 10 options for [headline/concept/offer]. Flag which ones are pattern-based and which ones are counterintuitive." Then you decide. Not the AI. Failure 2: You give AI your words but not your judgment My team built a custom GPT trained on hundreds of hours of my coaching. It kept improvising. Getting my frameworks wrong. Sending tone-deaf responses to struggling students. I shut it down after reading one sentence of a draft. AI will mimic your language. It cannot mimic your discernment. Build a judgment document. Not just examples of what you say. Examples of what you would never say. The edge cases. The emotional context. Feed that into every prompt. Prompt: "Here is my communication style guide: [paste doc]. Before you draft a response, tell me what emotional state my audience is likely in and how that should change your tone." Failure 3: You one-shot complex problems Most founders type one big question into ChatGPT and wonder why the output is generic. That's like hiring a new employee, giving them one vague instruction, and expecting boardroom-level output. AI works in layers. Each prompt should build on the last. Stack your prompts like a conversation. Prompt: "What are the top 5 urgent problems faced by [your target customer]? Give me evidence from Reddit, Amazon reviews, and forums." Then: "Of these 5 problems, which ones do people actively spend money trying to solve right now?" Then: "What does the current market offer to solve problem #1? Where are the gaps?" Each answer sharpens the next question. By prompt 4 you have real market intelligence. By prompt 1 you have nothing. AI doesn't fail. Vague direction fails. The founders killing it with AI right now aren't using better tools. They're asking better questions. That's exactly what I teach in my next free AI masterclass. I'll walk you through the exact prompt sequences I use to validate ideas, create systems, and move fast without building the wrong thing. Register here: https://buff.ly/6EU7loK

You want to use ChatGPT effectively for serious work? –Part II. LLM's limitations are serious, but you can work around them. First, Let’s restate chatGPT’s problems to make what you need to do more obvious. 𝐜𝐡𝐚𝐭𝐆𝐏𝐓 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Ⓐ As the conversation (session) grows, the earlier parts of the conversation get lower priority (or weight), likely diverting chatGPT’s “focus” away from key elements  Ⓑ The context window grows and exceeds some limit, and the earlier parts get deleted Ⓒ Some instructions confuses AI, defocuses and restricts it without giving you any indication—other than frustrating you. As LLMs support larger context windows, Ⓑ will affect only large-volume projects (like writing a long article, coding, analyzing a chapter, etc.). And Ⓐ & Ⓒ become the likely reasons for low quality. 𝐘𝐎𝐔𝐑 𝐦𝐢𝐬𝐬𝐢𝐨𝐧: 1️⃣  Keep the most important facts & instructions in the right place in the context window 2️⃣  Keep chatGPT focused and undistracted 3️⃣ Use prompts that use gen AI’s strengths and avoid its weaknesses4️⃣ Follow work habits and workflows that naturally keeps you productive The fourth one is the most important, and the hardest is the third one. One of the hardest Gen AI weaknesses to work around is negative instructions (like “don’t do this”, or “without that”, etc.). I’ll cover it in a special post. 𝐓𝐡𝐢𝐬 𝐬𝐞𝐫𝐢𝐞𝐬 will continue shortly with tricks that I will share to address all the above. #AI4Leaders #AI #chatGPT Ralph Aboujaoude Diaz #Strategy  𝐏.𝐒. 𝐋𝐨𝐭𝐬 𝐨𝐟 𝐩𝐞𝐨𝐩𝐥𝐞 said this series is very helpful. So, please spread the word and share it / repost it with your networks. First post is: https://lnkd.in/e35rjuAc 𝐏.𝐒. 𝐟𝐨𝐫 𝐭𝐞𝐜𝐡𝐢𝐞𝐬: If you use LLM API, you still have to contend with those problems!

After auditing 500+ ChatGPT prompts, I noticed one mistake almost everyone makes. They ask AI to summarize, instead of analyze. Leaders don’t need shorter texts. They need smarter insights. The secret isn’t what you ask for It’s how you frame the question. Here are 5 prompt frameworks that turn a simple summary into a strategic conversation: → Uncover hidden logic → Extract actionable takeaways → Simplify complex research → Build concise executive briefs → Translate policy into strategy Next time you summarize, don’t just shorten, sharpen the thinking. Save this post for your next ChatGPT session!