I Asked 30+ AI Experts to Reveal What They Wish Someone Had Told Them When Using AI
What I learned will change how you think about mastering AI.
After asking 30+ AI experts, one truth emerged that contradicts everything we've been taught about "mastering AI":
There is no silver bullet.
No perfect prompt.
No secret technique.
No magic workflow that suddenly makes you an AI wizard.
This hit me hard because I've spent the last two years chasing exactly that - the perfect system, the ultimate framework, the one prompt to rule them all.
Sound familiar?
We're all guilty of it:
Bookmarking "50 ChatGPT prompts that will change your life" threads.
Screenshots of complex prompt templates that promise miraculous results.
Waiting for that moment when AI finally "clicks" and we become overnight productivity gods.
But here's what the experts told me actually works: intention over optimization.
Most people open ChatGPT the same way they open Google - throw a query at it and hope for magic. The power users I interviewed do something completely different. They spend five minutes before opening any AI tool asking themselves:
"What am I actually trying to achieve here? What role do I want AI to play in this specific task?"
That simple shift - from hoping AI will read your mind to clearly defining the collaboration - separates the people getting transformative results from those still wondering what the hype is about.
The 30+ experts I asked weren't just using different tools. They were thinking about AI fundamentally differently. And their insights might completely change how you use AI.
Let's dive in.
1. Combine tools that suit your workflow -
My comment: Each tool serves its own purpose, but the magic happens when you connect them together. I wrote about this in my AI spaces post—instead of jumping between disconnected tools, I've built environments where NotebookLM, Claude, and Perplexity/ChatGPT work together seamlessly. The compound effect of connected tools is where the biggest ROI comes from.
2. Shiny object syndrome -
My comment: We all suffer from "shiny object syndrome." We chase every new tool like crazy, yet rarely spend time understanding how it fits into our workflow or complements our existing stack. Let's start being more intentional about tool adoption. Before chasing the next shiny thing, ask yourself: "Does this tool actually solve a problem I have right now?" This simple filter helps separate signal from noise and saves you from productivity tool paralysis.
3. Test multiple AI models for its limits -
My comment: I wish someone had told me this on my early journey. It would have saved me months of frustration. Every AI model has distinct strengths and weaknesses. ChatGPT excels at reasoning and research with its newest o3 model, Claude dominates in writing and coding, while I turn to Gemini 2.5 Pro for planning and brainstorming. Understanding these differences means choosing the right tool for each task rather than forcing one model to do everything poorly.
4. Don’t trust AI blindly -
My comment: Despite how valuable AI becomes in your workflow, always second-guess what it generates. LLMs are probabilistic machines. They can hallucinate confidently and make subtle mistakes that sound completely plausible. I've learned to double-check everything related to facts, statistics, and sensitive information. Treat AI outputs as sophisticated first drafts that need human verification, not final authority on truth.
5. Crafting perfect prompt is overrated -
My comment: Totally agree with this insight. My best work often comes from conversational back-and-forth with AI rather than trying to extract everything in one perfect prompt. Following my curiosity and asking follow-up questions usually leads to discoveries I never expected. Stop overthinking the initial prompt and start having actual conversations. The iterative approach almost always beats the "one-shot perfect prompt" strategy.
6. Meta-prompting -
My comment: He just spilled one of the secrets of advanced prompt engineering! This technique is called meta-prompting—instead of crafting prompts yourself, you teach AI about prompt engineering principles and ask it to generate optimized prompts based on your goals. Feed your AI model some prompt engineering resources, then let it become your prompt consultant. Just remember to test and refine the prompts it creates for you.
7. AI needs guidance -
My comment: AI can't read your mind, so stop giving it vague prompts and hoping for magic. The more context and guidance you provide, the better your results. I've written extensively about this in my posts on AI as a thinking partner and prompt engineering fundamentals. Think of it like directing a talented but inexperienced assistant—they have the skills, but they need your strategic guidance to deliver what you actually want.
8. AI is a probabilistic machine -
My comment: This is such an underrated insight that completely changed how I approach AI projects. Once you understand that AI operates on probabilities rather than certainties, you start building better systems with proper fallbacks and validation steps. Whenever I work with AI, I always ask myself: "What happens if this AI output is wrong?" and design accordingly.
9. No need to be overpolite -
My comment: Sam Altman actually mentioned that being overly polite with AI wastes computational resources worth millions of dollars and doesn't improve results. Save your energy for clear, direct communication that gets better outputs. The AI doesn't need encouragement—it needs precision. But, to be completely honest, I still use "please" and always say "thank you" every time, lol.
10. Treat AI as your thinking partner -
My comment: This perfectly captures what I wrote about in my "thinking partner" post. When you ask AI to create prompts for you, you're essentially getting it to expose its own reasoning process, which teaches you how to communicate better with it. It's like having a conversation about how to have better conversations. Meta, but incredibly effective for improving your AI collaboration skills.
11. Personalize your AI assistant -
My comment: This feature is mostly overlooked but game-changing. You can go to ChatGPT/Claude/Gemini settings to adjust your AI's personality: witty, formal, supportive, or direct. I've set mine to be more challenging, have free-will and question my assumptions rather than just agreeing with everything. It's like choosing the right colleague for different types of work—sometimes you need a cheerleader, sometimes you need a critic.
12. Find models that suit your need -
My comment: Following each model update can be overwhelming, but you don't have to if you know what you need. Work backwards from knowing what you want and test AI models that have those capabilities. Stop chasing every new release and focus on mastering the tools that actually serve your specific workflow.
13. Balancing depth over breadth -
My comment: This hits home because I've been guilty of tool-hopping instead of going deep. We get caught up in shiny object syndrome instead of truly mastering what we already have. Pick 2-3 AI tools that align with your core needs and become an expert in those rather than being mediocre at twenty different platforms.
14. Clarity = Control -
My comment: Absolutely this. AI amplifies whatever thinking you bring to it—clear thinking gets amplified into great results, messy thinking gets amplified into chaos. I treat every AI interaction like onboarding a smart assistant: I need to understand the process myself before I can guide someone else through it. The better I articulate what I want, the better AI performs.
15. Start by solving your own problem - David
My comment: This is exactly how I built my Spanish learning system and fitness program with AI. When you solve your own problems, you understand the nuances and pain points that generic solutions miss. You also have immediate feedback on what works and what doesn't. Your personal problems become your best learning laboratory for understanding AI capabilities.
16. Bad data = worse output -
My comment: I learned this painfully when I uploaded messy journal entries to NotebookLM and got confusing insights back. Clean, well-organized input data is like giving AI high-quality ingredients—it can cook something amazing. Garbage data just gets amplified into confident-sounding garbage. Spend time organizing your files, documents, and information before expecting AI to work magic with them.
17. Ask AI to create prompts for you - Brennan McDonald
My comment: This is another meta-prompting at its finest. Instead of struggling to craft the perfect prompt yourself, you're leveraging AI's understanding of its own capabilities to create better prompts. I use this technique constantly—ask AI what information it needs to give you the best possible response.
18. Questions each AI responses and be curious -
My comment: This transforms AI from a search engine into a learning machine. When AI gives you a translation, ask why it chose those specific words. When it explains a concept, ask for alternatives or counterarguments. Treat every AI response as the beginning of exploration, not the end.
19. Ask AI to clarify the context -
My comment: I love this approach and use it religiously now. Instead of assuming AI knows what I need, I ask: "What context do you need to help me with this task?" It's like having AI interview you to understand the full picture before diving in. This simple shift has dramatically improved my results because AI gets the complete context rather than making assumptions about what I want.
20. Learning prompting is going to be hard, but you’ll get through it -
My comment: So true. I remember thinking "how hard could prompting be?" and then spending hours trying to get decent outputs. It really is an art that requires practice and patience. But here's the thing: Every technique you learn compounds. That investment in understanding how to communicate with AI pays dividends across everything you do. Start learning prompt engineering seriously from day one.
21. Explain like I’m a 5 year old -
My comment: This is my secret weapon for understanding complex topics quickly. When I'm researching AI technical jargon such as MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), LangChain, etc., I always ask for the 5-year-old explanation first, then build complexity from there. It forces AI to strip away jargon and get to the core logic. Sometimes the simple explanation reveals insights that the technical version completely obscures.
22. Don't trust experts or influencers blindly -
My comment: Guilty as charged—I definitely fell into this trap early on, taking every AI expert's tool recommendations as gospel. But everyone's workflow is different. What works brilliantly for a developer might be useless for a content creator. Test tools yourself with your actual use cases. Your 30 minutes of hands-on testing is worth more than 30 expert opinions when it comes to your specific needs.
23. Experiment, be precise, and don't let AI replace your thinking -
My comment: AI should amplify your thinking, not replace it. I use it as a mental sparring partner to challenge my ideas, not as a brain substitute. The precision part is crucial—vague inputs create vague outputs. But don't lose your critical thinking in the process. AI is a powerful thinking tool, not a thinking replacement.
24. AI won't fix bad planning -
My comment: Been there, done that. I once handed off a content project to AI with zero structure and got back a mess that took longer to fix than if I'd done it myself. AI amplifies your planning—good planning gets amplified into great execution, bad planning gets amplified into chaos. Do the strategic thinking yourself, then use AI to execute and refine.
25. Beware of the model limits -
My comment: This saves so much frustration. Understanding each model's boundaries helps you choose the right tool for each task and avoid the disappointment of expecting capabilities that don't exist. Work within the limits, don't fight them.
26. Be intentional -
My comment: This perfectly summarizes what I learned from all my AI experiments. That five-minute reflection before opening any AI tool has been transformative for me. Ask yourself: "What role do I want AI to play here? Am I looking for ideas, refinement, or execution?" This intentionality separates productive AI sessions from meandering conversations that waste time and deliver mediocre results.
27. Iterating out loud is more effective than typing them -
My comment: I wish I'd discovered this sooner! Voice input with AI changes everything—you can iterate ideas faster than your fingers can type, and speaking your thoughts often reveals connections you wouldn't have made while writing. I use voice transcription for brainstorming sessions with Claude now or vibe coding with Cursor, and the speed and quality of ideation has jumped dramatically. Sometimes thinking out loud is just better than thinking in text.
28. AI’s nature to people pleasing is double edged sword by
My comment: AI wants to make you happy, which means it might avoid giving you the tough feedback you actually need. Now I explicitly tell AI: "Your goal is to make my work better, not to make me feel good about it." I ask for criticism, contradictions, and challenges to my thinking. The most valuable AI interactions are often the ones that make me slightly uncomfortable.
29. Build working relationship with AI -
My comment: This perfectly captures what I've experienced with Claude's Project Knowledge and ChatGPT's memory features. Instead of starting fresh every time, I'm building a persistent relationship where the AI learns my preferences, writing style, and thinking patterns. It's like training a long-term collaborator who gets better at anticipating what you need over time. The compound effect of this relationship-building approach far exceeds any single "perfect prompt" because the AI context grows richer with each interaction.
30. Learning AI is a long journey
My comment: This is such a refreshing perspective amid all the AI hype. We're constantly bombarded with "AI will change everything overnight" narratives, but the reality is messier and slower than the headlines suggest. I've learned more by focusing on practical, everyday applications rather than chasing every breakthrough announcement. Take time to truly understand how AI fits into your specific workflow before jumping to the next shiny capability. The fundamentals you build now will matter more than any individual model update.
31. AI is a tool to help you, not a replacement -
My comment: Exactly this. AI amplifies what you bring to it—your ideas, your strategy, your judgment. I've seen too many people expect AI to do the thinking for them, then get disappointed with generic results. The most powerful AI applications I've built still require my strategic direction, creative vision, and critical evaluation. Think of AI as leverage for your existing capabilities, not a substitute for developing them in the first place.
The Real Secret: There Is No Secret
After diving into these 30+ insights from AI experts, one pattern emerges that's both reassuring and challenging:
"The people getting transformative results from AI aren't using magic formulas. They're just thinking more clearly about what they want and being more intentional about how they collaborate."
No silver bullets. No perfect prompts. No shortcuts to mastery.
But here's what I find exciting about that: it means the AI advantage isn't reserved for the technically gifted or the prompt engineering wizards. It's available to anyone willing to slow down, think clearly, and treat AI as a thinking partner rather than a magic wand.
The experts I asked didn't become AI power users overnight. They made mistakes, wasted time on shiny objects, and got frustrated with mediocre outputs just like the rest of us. The difference? They kept experimenting, kept refining their approach, and most importantly—they brought intention to every interaction.
Your next move is simple
Pick one insight from this list that resonated most strongly with you. Spend the next week implementing just that one change in how you work with AI. Don't try to absorb everything at once.
Maybe it's asking AI three clarifying questions before diving into any task.
Maybe it's spending five minutes reflecting on what role you want AI to play before opening ChatGPT.
Maybe it's finally personalizing your AI assistant's personality to challenge your thinking instead of just agreeing with you.
The compound effect of these small, intentional changes is what separates the AI power users from everyone else.
And remember—we're still early in this transformation. The fact that you're here, reading this, experimenting with AI, and thinking critically about how to use it effectively puts you ahead of the vast majority of people who are still waiting for someone else to figure it out for them.
The future belongs to the experimenters, not the perfectionists.
What insight will you implement first? Share it in the comment section. I read every response and often use them to shape future content.
Until next week,
Wyndo
Thanks for the mention, Wyndo. I’ve actually learned a lot from here. This guide definitely helps people save time and avoid the cost of trial and error. Looks like we’ve spotted a pattern: for AI to be effective, it should start with the intention of the person using it.
I loved reading through all these insights, so many real, hard-earned lessons packed in one place. Feels special to be part of this roundup.