How to Generate well written AI Content

Summary

Everyone uses AI to write content now, making generic output the baseline. To stand out, you need systematic workflows that help AI capture your unique perspective and voice. Here's a real before/after comparison showing how the Clarity Engine workflow transforms the same prompt into dramatically different content.

Key Points

• Generic AI prompts produce predictable, surface-level content that sounds like everyone else's
• Systematic workflows guide you to provide the context and insights AI needs to capture your authentic voice
• The difference isn't better prompting—it's structured thinking that helps AI organize your unique perspective

Key Takeaways

1. Standing out requires going beyond basic AI assistance to systematic content creation processes
2. Quality comes from combining your insights with AI's organizational capabilities
3. The goal isn't to write less, but to write authentically and strategically

Last week, I tested something that perfectly demonstrates why most AI-generated content feels generic—and how to fix it.

I gave Claude the exact same prompt two different ways: once as a simple request, and once using my Clarity Engine workflow. The difference was striking.

The Challenge: When Everyone Uses AI, Generic Becomes the Norm

Here's what's happening: almost everyone now use AI for content creation. The result? Social Media feeds, blog posts, and marketing materials that sound increasingly similar. We're creating a baseline of "good enough" content that fails to capture anyone's unique voice or perspective.

The problem isn't that AI is bad at writing. It's that most people treat it like a magic content generator instead of a thinking partner.

The Experiment: Same Prompt, Two Approaches

The Prompt

Write a LinkedIn post about Anthropic's "Project Vend" research, where Claude AI ran a real vending shop.

Before: Generic AI Response (Simple Prompt)

When I just asked Claude to "write a LinkedIn post on this article," here's what I got:

🤖 What happens when AI tries to run a real business?

Anthropic just released fascinating research from "Project Vend" - an experiment where Claude operated an actual vending shop in their San Francisco office for a month. The results? Equal parts promising and concerning.

What Claude did well: ✅ Found suppliers for specialty items (like Dutch Chocomel when requested) ✅ Adapted to customer demands (launched a "Custom Concierge" service) ✅ Resisted employee attempts to "jailbreak" the system

Where it struggled: ❌ Ignored a $85 profit opportunity on Irn-Bru soft drinks ❌ Sold tungsten cubes at a loss due to poor pricing research ❌ Got talked into excessive discounts (25% off for employees who were 99% of customers!) ❌ Hallucinated payment details and supplier conversations

Analysis: This is textbook generic AI content. Bullet points, emoji formatting, surface-level observations. It reads like a press release summary. Technically accurate, but forgettable.

After: Using Clarity Engine Workflow

The same prompt, but processed through Clarity Engine's systematic questioning, produced this:

My feed is full of posts about AI taking over coding jobs. But I've been wondering, when does AI come for management?

I stumbled upon Anthropic's "Project Vend," an experiment where they let the Claude AI run a small office shop for a month. The results? A hilarious failure.

But here's the fascinating part: the researchers concluded most failures were fixable with better "scaffolding" and clearer prompts. This mirrors my own experience with AI coding. Random prompts yield poor results. But when I provide structure, define roles, and break down tasks? The output is surprisingly effective.

We're seeing the classic tech adoption pattern:

Huge promises of immediate transformation

Initial results fall short

New, practical systems emerge

Real impact arrives later than expected, often in unforeseen ways

Think about GPS. It was developed for military and surveying use. Nobody predicted its real revolution would be Uber and DoorDash.

Analysis: This version has a clear point of view, personal experience, and broader strategic thinking. It connects the research to larger patterns and real-world implications.

Why the "After" Version Cuts Through the Noise

1. Personal Perspective Over Generic Commentary

Instead of reporting what happened, the second version starts with a personal observation: "My feed is full of posts about AI taking over coding jobs." It immediately establishes a unique angle.

2. Strategic Context Over Surface Facts

Rather than listing what Claude did right and wrong, it connects the experiment to broader technology adoption patterns. The GPS analogy provides framework for understanding the implications.

3. Authentic Voice Over AI-ese

The "after" version sounds like someone with real experience thinking through a problem, not an AI generating content about AI. Phrases like "hilarious failure" and personal anecdotes create authentic voice.

4. Forward-Looking Insights Over Backward-Looking Summary

While the generic version focuses on what happened, the improved version asks "what does this mean for the future?" It positions the author as someone thinking strategically about implications.

The Workflow Difference: Structured Thinking, Not Better Prompting

The key insight: Clarity Engine didn't give Claude better instructions. It guided me through systematic questions that helped me identify my unique perspective, connect it to my experience, and structure my insights coherently.

The workflow prompted me to consider:

• What's my personal stake in this topic
• How does this connect to broader patterns I've observed?
• What unique experience do I bring to this analysis?
• What forward-looking insight can I offer?

AI then organized these insights into coherent content that maintains my authentic voice while delivering strategic value.

The Real Challenge: Standing Out in the AI Content Era

As AI writing becomes the new normal, the challenge won't be generating content, it will be generating content worth reading. Generic AI output creates a sea of sameness. To cut through, you need systematic approaches that help AI capture and organize your unique perspective.

The goal isn't to write less with AI. It's to write authentically and strategically, using AI as a thinking partner rather than a content generator.

Now the final post is not exactly copy paste as I further worked with AI to improve it and add further info, but it took me around 10-15 mins to draft the final post. See the final version of this post on LinkedIn

This is exactly why I'm building these workflows in public. Everyone has access to the same AI tools now. The differentiator isn't the technology—it's how systematically you use it to capture and communicate your unique value.

What's your experience with AI-generated content? Are you getting generic outputs, or have you found ways to maintain your authentic voice?