You’re Using AI Wrong

I’ve trained teams at Fortune 500 companies to use AI. I’ve watched thousands of people go from “I use ChatGPT sometimes” to “I can’t imagine working without AI.”

The biggest predictor of success? Exposure. People who see what’s possible start using AI differently.

Here’s the complete map—from first prompt to building autonomous agents.

Level 0-1: Chat Interfaces and Voice

Most people already use chat interfaces. Most people use them wrong.

What most people miss about Claude:

The system prompt matters enormously. Before your first message, write instructions. Who you are. What you’re working on. How you want responses formatted.

Artifacts are underutilized. When Claude creates code or documents, it puts them in a separate panel you can iterate on. Ask explicitly: “Create an artifact with…”

Projects change everything. Create a project for each major area of work. Upload relevant files. Write project-specific instructions. Every conversation starts with context.

The voice unlock:

The highest-leverage people I know talk to AI more than they type to it.

Tools like Whisper Flow ($8/month) let you hold a hotkey, speak, release, and get transcription sent directly to an LLM with custom prompts. Walking to get coffee? Voice a rough draft. Driving? Think through a problem out loud.

Once you set this up, you’ll use AI constantly.

Level 2-3: Deep Research and Writing

This is where AI goes from “helpful assistant” to “force multiplier.”

ChatGPT Deep Research spends 5-30 minutes browsing the web and delivers comprehensive reports with citations. The output rivals what a human analyst would produce in hours.

Use it for:

• Market research and competitive analysis
• Due diligence on companies
• Technical comparisons
• Major purchase decisions

For writing, Claude is the best AI writer. But the trap is obvious: AI writing is recognizable—polished but bland.

The goal isn’t to publish Claude’s output. It’s to get to a good first draft faster, then make it yours.

Create a Claude Project with 3-5 examples of your best writing, a description of your voice (“Direct. Short sentences. No buzzwords.”), and your audience. Every conversation in that project matches your voice.

Level 4-5: Meetings and Visual Creation

Tools like Notion AI Meeting Notes capture, transcribe, and summarize meetings directly in your workspace. No bot presence. Notes connect to your tasks. AI can assign tasks and create follow-ups.

For images, Google’s Nano Banana (in Gemini) is the best combination of quality and ease of use. Conversational editing: “Make the background darker.” “Remove the person on the left.” Character consistency across multiple images.

Level 6: Automation with AI

This is where AI starts working while you sleep.

Zapier is the most accessible way. 7,000+ app integrations, built-in ChatGPT steps, natural language builder.

Automations that actually matter:

Content repurposing: New blog post triggers ChatGPT to create a Twitter thread and LinkedIn post, then schedules them.

Lead qualification: Form submission goes to ChatGPT for scoring, routes to sales if hot, nurture if warm, archive if cold.

Email processing: Incoming emails get categorized, urgency scored, tasks created automatically.

Meeting prep: Calendar event triggers web search for attendee info, ChatGPT creates a brief, emails it to you an hour before.

Level 7: Vibe Coding

You don’t need to know how to code to build software anymore.

Lovable is an AI-powered app builder. Describe what you want, get a working React application.

What you can build:

• Landing pages
• Internal tools and dashboards
• Customer portals
• Simple SaaS products
• Prototypes for investor demos

The mental shift: stop thinking in features, start thinking in descriptions.

Build structure first (navigation, pages, layout) before content. Reference known designs (“Design like a Stripe dashboard”). Be explicit about what NOT to touch.

Level 8-10: Engineering and Beyond

For people who code—or want to learn—these levels multiply output by 10x.

Claude Code is a command-line tool for agentic coding. Give it a task, it figures out what files to read and write, runs commands, iterates until done. The meta-skill: it’s only as good as your ability to describe what you want clearly.

Building agents means AI that takes actions in the world. Every agent has three parts: a trigger, a reasoning loop, and tools. Start with a research agent that searches, compares, and synthesizes. This teaches the core loop without complexity.

RAG systems give AI access to your proprietary data. Documents get chunked, embedded as vectors, stored in a database. When someone asks a question, similar chunks get retrieved and fed to the LLM. This is how you build AI that knows what you know.

Model Context Protocol by Anthropicis becoming the universal standard for connecting agents to tools. Build MCP servers once, any AI can use them.

The Meta-Insight

Tools matter less than knowing when to use which. That skill comes from trying everything, developing taste, and building habits.

The tools will change. The categories won’t. Learn patterns, not just products.

Start where you are. Master your current level before moving up.

FAQ

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