Why Most People Get Mediocre Results From AI

I’ve spent a long time studying how the top 1% of AI users actually work. The people shipping products, building businesses, and saving thirty-plus hours a week don’t use AI differently because they’re smarter. They use it differently because they’ve internalized a specific set of habits.

Most people open ChatGPT, type something vague, get a generic answer, and wonder what the fuss is about.

The gap isn’t intelligence. It’s methodology.

Here’s what I’ve found: power users treat AI not as a search engine or chatbot, but as a combination of employee, tutor, and strategic partner. They follow a specific progression of skills that builds on itself.

Let me walk you through exactly how to do the same.

Here’s what you’ll get from this guide:

• A five-line prompt template that covers 99% of prompt engineering
• How to turn AI into your toughest critic, not your yes-man
• Why skills matter more than copying someone else’s setup
• The real difference between a consultant and an employee — and how MCP closes the gap
• Why repeating yourself to AI actually makes it smarter

Step 1: Master Prompt Engineering With One Simple Template

Prompt engineering sounds intimidating. It’s not. It’s just the difference between giving someone a vague request and expecting a detailed report versus handing them a structured brief.

Most people prompt like this: “Make it better.” Three words. That works about as well as you’d expect.

Here’s the template I use in my head for 99% of my prompting. It takes thirty seconds to internalize:

Line one: “You are a top 0.1% expert in [field].”

Fill in the blank. Venture capital, copywriting, launching funnels, whatever.

Line two: The task. Keep it simple. “Research this and output a table.” “Draft a marketing plan.” Whatever you actually need.

Line three: Context. Dump everything you’re willing to share. What you’ve tried, conversion rates, ROI, budget, time constraints. The more context, the better the output.

Line four: Constraints. Budget, time, faceless branding, specific goals. Specify it so AI knows what kind of information to gather.

Line five: The magic phrase I add every time: “Ask me clarifying questions one at a time until you’re 95% confident you can complete the task successfully.”

That last line is gold. It forces AI to interrogate your thinking before producing anything. Half the time I realize my original request was vague and confused. The clarifying questions sharpen not just the AI’s output but my own thinking.

Step 2: Turn AI Into Your Intellectual Sparring Partner

Here’s something most people never do. Instead of using AI to agree with your ideas, use it to beat them up. This is the same move I break down in 3 simple tips to turn Claude into a genius thinking partner.

I regularly tell Claude: “Here’s what I’m thinking. Argue with me. Why am I wrong? What are my blind spots? Where are my weakest assumptions?”

Append phrases like:

• “Identify the weak points an investor would critique.”
• “Rip this business plan apart.”
• “Based on everything we’ve discussed, where am I missing data?”

This changes the entire nature of your conversations. Instead of AI telling you what you want to hear, it becomes the candad advisor you wish you had on your board.

When you have five appealing options, how do you choose in real life? You don’t ask your yes-man. You ask people who’ll give you honest feedback. AI can play that role if you let it.

Step 3: Use AI as Your 24/7 Tutor

This one surprises people. I get questions constantly from people stuck on a tutorial asking for help. The answer is always the same: ask AI.

Open ChatGPT or Claude. Screenshot the error. Paste it. Say: ” I’m trying to build this, I got to this step, and now I see this. What do I do?”

AI is surprisingly good at interpreting screenshots and walking you through fixes. It’s infinitely patient. It won’t judge you for asking the same question four times. It has the entire internet at its fingertips.

Here’s my challenge. For the next two weeks, ask AI first before you ask any person. Every question. I promise you’ll start solving problems faster than waiting for a YouTube comment reply.

Step 4: Build Reusable Skills

Within the Claude ecosystem, skills are repeatable tasks you define. Think of them as pre-built playbooks AI follows automatically.

Let me give you a real example from my content workflow. I have separate skills for:

• Research
• Script outlining
• Script writing
• Quality checking
• Fact checking

Each skill contains context about what good looks like for that specific task. When I ask Claude Code to write a YouTube script, it automatically uses these skills in the correct order because it knows what each one does.

The key insight: don’t search for generic skills someone else made. Create your own. Here’s my favorite prompt:

“Based on our conversation, create three skills. Ask me clarifying questions one at a time until you’re 95% confident you can complete the task successfully.”

Claude analyzes your conversation, finds repeatable tasks with lots of context, and builds skills around them. It’s that simple.

Step 5: Give AI a Memory So You Never Repeat Yourself

Memory goes by different names depending on the tool. In ChatGPT, it’s projects. In Claude, also projects. In Claude Code, it’s the CLAUDE.md file.

Think of it as a folder containing everything important about your business. You load it once, and AI references it in every future conversation.

For Claude Code, my CLAUDE.md file contains my coding standards, architecture decisions, and constraints. In a marketing context, it would hold your brand voice, content types, and product details.

My favorite trick: never update it manually. Ask AI to do it.

After significant conversations, I run a skill called /learn that tells Claude: “Reflect on this conversation and extract anything important that should be updated in CLAUDE.md or made into a separate skill.”

Do this consistently, and your memory file evolves with your business. It’s a massive source of leverage.

Step 6: Repeat Your Most Important Instructions

This sounds counterintuitive. But research has shown that repeating key instructions to AI produces better results.

I have a shortcut I run at the start of every Claude session that forces it to reread my CLAUDE.md file. And I repeat the same instruction multiple times within conversations.

For example, my most-used development skill is called Q-mean. It says: “Review the code and make sure it introduces minimal changes to the codebase.”

I run it two or three times before letting Claude implement anything. Why? Because my biggest fear is AI slop code that breaks other things or code I don’t understand. Repeating this instruction keeps it top of mind.

Step 7: Plan Before You Build

Here’s something that changed everything for me: I spend 80 to 90 percent of my time in plan mode.

Instead of letting AI start building immediately, I force a back-and-forth first. I ask questions. I challenge approaches. I consider alternatives. All while using the sparring techniques from step two.

Only when I’m genuinely happy with the plan do I let AI execute.

And here’s the critical part: I monitor what AI does during execution. Especially for coding. If it heads down the wrong rabbit hole, I abort early rather than dealing with broken code later.

Step 8: Connect AI to Your Real Tools With MCP

Model Context Protocol, or MCP, is what transforms AI from a consultant that tells you what to do into an employee that actually does the work.

Without MCP, ChatGPT gives you instructions. With MCP, it can pull last week’s sales from Stripe, summarize support tickets from Intercom, and draft a Monday report into Notion — all without you leaving the chat.

You can connect Google Drive, Airtable, Slack, email, CRMs, and dozens of other tools.

If you’re not tehnical, don’t worry. Just ask AI: “Here are the apps I use. Which ones have MCP? Walk me through setting them up.”

Claude will literally guide you step by step.

Step 9: Stack Skills With MCP for Your Own AI Employee

Now we’re combining everything. Skills tell AI what to do. MCP gives it the tools to do it. Together, they create the closest thing to an AI employee I’ve ever experienced.

Here’s a real example. I have a skill called /crosspost that:

1. Searches my Google Drive for finished TikTok drafts
2. Transcribes each video
3. Wites platform-specific captions
4. Generates visuals for select videos
5. Schedules everything to social media via MCP
6. Logs everything in Airtable

That’s a twelve-step process that used to take hours, now running automatically. And unlike traditional workflow automation like n8n or make.com, I can add exceptions on the fly. “Process 18 of these videos normally, but publish those two right now.”

That flexibility is what makes it feel like a real employee.

Step 10: Leverage Mobile Apps to Stay in Sync

Every major AI tool has a mobile app, and most people sleep on them. You can start a conversation on your laptop and continue it on your phone. No starting over.

Even better, Claude Code just released remote control. You can run Claude Code from your phone, with access to your local files. Ask it to find that photo you took yesterday and post it to social media. It’ll do it — with your permission confirmation, of course.

Step 11: Use GitHub to Manage Your Work Safely

If you’re using Claude Code, GitHub is essential. It’s not just version control — it’s a sandbox.

Imagine having a main copy of your project and a branch where you can experiment freely. Mess up the branch? No problem. Just go back to the main copy.

Happy with your experiments? Merge them into the main. It’s like a really fancy floppy disk with a playground built in.

Step 12: Put in the Reps

Here’s the uncomfortable truth. None of this matters if you don’t actually practice.

I’ve used Claude Code for hours every day for a year. That’s what reps mean. Watching tutorials without following along is like watching people work out and expecting to get fit.

You’re going to fail. You’ll accidentally delete stuff. You’ll restart projects five times. That’s part of the work.

But that’s also what separates the people getting results from the people still asking AI to “make it better.”

Frequently Asked Questions

What’s the most important prompt engineering hack? Adding “Ask me clarifying questions one at a time until you’re 95% confident” at the end of your prompt. It forces AI to interrogate your thinking and reveals gaps in your original request.

How do I get AI to be honest with me instead of just agreeing? Tell it directly: “Argue with me” or “Rip this plan apart.” Aend phrases like “Identify my weakest assumptions” or “Find the blind spots an investor would critque.” AI adapts to the tone you set.

What are skills in Claude and why should I create my own? Skills are repeatable task definitions with your specific preferences baked in. Don’t download generic ones from directories — ask Claude to analyze your conversations and build skills tailored to your workflow.

What does MCP actually do? It connects AI to your real tools — Google Drive, Stripe, Slack, Notion — so AI can pull data and take action instead of just giving you instructions. That’s the difference between a consultant and an employee.

Why does repeating instructions to AI help? Research from Google showed that repeating key instructions keeps them top of mind for AI, which produces better, more consistent results. I run my most important instruction two or three times before letting AI execute.

Do I need to be a developer to use these hacks? Not at all. Every step builds on the one before it. Start with prompt engineering, then move to sparring, tutoring, and skills. Tools like GitHub and MCP sound technical, but you can ask AI to walk you through setup step by step. For a beginner-friendly tour of the five Claude modes and the PRIME framework, see how to use Claude better than 99% of people.