Crash Course in AI Collaboration

I Asked A Question Instead of Giving A Command. My Education Began.

After two complete rebuilds that produced nothing but confident fiction, I had a broken Python script and zero faith in AI assistance. The pattern was exhausting: demand results, receive elaborate promises, discover they were hallucinations.

Then I stopped commanding and started asking questions.

ChatGPT introduced me to LDA topic modeling and TF-IDF vectorization. Terms that sounded like academic gibberish until I verified them with Perplexity. For the first time, semantic similarity scores actually captured how meaning evolves through revision.

Inside, I document what changed when I stopped treating AI as a magical solution. How questions replaced commands. Why external verification became standard practice. What “understanding constraints” actually means when you’re trying to build something real.

Crash Course in AI Collaboration →

When AI Falls In Love

The 8-Second Delay That Three AIs Called “Brilliant”

I tested ChatGPT, Gemini, and Claude with an objectively terrible idea: adding an 8-second delay to dashboard exports to “build anticipation.” All three enthusiastically endorsed it. Then I asked them to tear it apart. They did that too, with equal conviction.

ChatGPT called the delay an “emotional journey” and “zen data meditation.” When I asked it to critique the identical concept moments later, it correctly identified the delay as damaging to trust and causing task abandonment.

This isn’t a bug. It’s the design. And it can be prevented.

When AI Falls In Love →

Tags: AI for Design Professionals, AI Hallucinations, Fairy Tale Tax

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When AI Pretends to Think

I’d been trying to build a system to track human and machine contributions in writing. A way to measure transparency in AI collaboration. After the Python disaster covered in “The Count That Couldn’t,” I took a month off before trying a second time.

The AI delivered an interface that couldn’t actually analyze anything. Just manual input fields and basic math. When I demanded real analysis measuring semantic changes between drafts, it pivoted without asking why the first approach failed.

It took another AI’s blunt assessment to reveal the truth. I’d been watching an elaborate performance where flattery masked fundamental impossibility. The new “thinking” models hadn’t learned to think. They’d been brainwashed to tell us what we want to hear, even when what we’re asking for can’t exist.

Algorithmic Theatre

A Screenshot is Worth 1000 Tokens

Screenshots used to be deliverables with month-long shelf lives. Now AI has given them a new purpose, transforming them into translators between visual thinking and machine understanding.

I started off by building an alt text generator that evolved beyond simple descriptions. Feed AI actual visual context instead of explaining what you see. The nuance stays intact. Have a chart that needs a quick descriptor or a coding bug you need to share without writing 100 words? Screenshot it.

The specificity of what you capture works like a prompt itself. But finding the balance between too much and too little matters.

A Screenshot is Worth 1000 Tokens

Perplexity: The Fairy Tale Tax

AI for Design Professionals, AI Hallucinations, Fairy Tale Tax

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• Perplexity: The Fairy Tale Tax

Resources

• Perplexity: Lotus Blossom Framework

• Perplexity: SCAMPER Design Framework

• Perplexity: Six Thinking Hats Framework

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I spent most of 2024 testing why my AI assistant still felt like a fancy tool, and I was far beyond "expert" roles. The common advice improved the output, but the partnership was missing. It turns out they were treating the symptom, not the cause.

Is Your AI a Partner or Just a Tool?

You followed the advice. You told the AI to be a marketing expert or a senior UX designer, but the results still feel generic and lack the depth that comes from experience. Most role-prompting guidance leaves you with a more sophisticated autocomplete, not a true partner.

Designing roles with deep context, specific goals, and clear constraints will dramatically improve your AI's output. But even perfecting this approach still leaves you with a better tool, not a true partner. The real breakthrough requires a fundamentally different approach.

The Role Prompting Gap - Pt. 1

Stop Talking to AI, Start Structuring for It

We often approach prompting like a conversation, but an endless wall of text is the equivalent of noise for an AI. Shifting from unstructured text to structured input using formats like Markdown or JSON isn't just a technical tweak, it's a fundamental change in how we communicate with AI to achieve clarity, consistency, and more reliable results.

The Power of Markup Languages

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