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How one-person growth teams can leverage AI to produce high-performing content at scale
After joining a startup as the sole member of the growth team, I faced a seemingly impossible challenge: create enough high-quality content to drive significant user acquisition across multiple platforms. With limited resources and unlimited ambition, I turned to AI as my secret weapon.
The results? Over 25 million views across platforms and 100,000+ new users in just one year.
But this success didn’t come from randomly plugging prompts into ChatGPT. It required developing a systematic approach to AI prompting through countless hours of trial and error.
If you’re struggling to maximize AI’s potential for content creation, here are the five most valuable prompting principles I’ve discovered after a year in the trenches:
1. Prompt Chains > One-Shot Prompts
The Problem: Many people try to achieve complex outputs with a single, lengthy prompt. They cram context, instructions, examples, and desired outcomes into one massive block of text, then wonder why the AI’s response feels disconnected or misses crucial elements.
The Solution: Break your prompting process into logical chains that build upon each other.
AI works best when it has comprehensive context but can process it step by step. If you’ve ever experienced AI ignoring parts of your instructions, it’s often because you’re asking it to juggle too many tasks simultaneously.
For example, when creating LinkedIn content, my process looks like this:
- First prompt: Analyze my LinkedIn profile and target audience to develop a content strategy
- Second prompt: Based on this strategy, create 3-5 content pillars
- Third prompt: For each pillar, develop specific content angles
- Fourth prompt: Review my rough draft for a specific angle
- Fifth prompt: Polish the final content piece for publishing
This methodical approach produces significantly better results than trying to go from “I need LinkedIn content” to a finished post in one step.
2. “Iterate Like Crazy. Good Prompts Aren’t Written; They’re Rewritten.” – Greg Isenberg
The Problem: Most people treat prompting as a one-and-done process. When they don’t get the results they want, they blame the AI rather than their prompt.
The Solution: Approach prompting as an iterative craft.
The difference between mediocre and exceptional AI outputs often comes down to how much you refine your prompts. When I get a response I like, I immediately ask: “How can I improve this prompt for better results next time?”
Some of my most valuable iterations include:
- Adding specificity about tone, length, and format
- Incorporating constraints (“Write this without using industry jargon”)
- Including clear examples of what to avoid
- Breaking down complex requests into smaller, more digestible components
Over time, these iterations compound into prompting patterns that consistently deliver high-quality results. I keep a “prompt library” of my most successful prompt chains for different content types, which I continuously refine.
3. AI Is a Rockstar at Copying—Give It Examples
The Problem: Generic prompts produce generic content that doesn’t match your authentic voice or brand style.
The Solution: Leverage AI’s pattern-matching abilities with strong examples.
One of my most successful applications of this principle was ghostwriting for my founder. For a month, I maintained email newsletters with a 30-50% open rate—significantly above industry averages.
My process:
- Draft the content in my natural voice
- Collect 3-5 recent posts written directly by the founder
- Prompt the AI: “Rewrite my draft to match the founder’s tone, vocabulary, sentence structure, and unique communication style based on these examples.”
The founder was genuinely shocked at how accurately the content captured her voice. She initially thought I had developed an uncanny understanding of her communication style, not realizing it was AI-assisted.
This “example-driven” approach works for any voice or style you want to emulate. The key is providing diverse, high-quality examples that represent the target style.
4. Know the Strengths of Each Model
The Problem: People treat all AI models as interchangeable, not realizing each has unique capabilities and limitations.
The Solution: Develop a working knowledge of different models’ strengths and use them strategically.
The AI landscape is constantly evolving, with models optimized for different tasks:
- Claude 3 Opus: Exceptional at reasoning, analysis, and understanding nuance. I use it for developing brand strategies, analyzing audience personas, and creating thoughtful long-form content.
- GPT-4o: Balanced performance for general writing tasks. Perfect for everyday content creation, social media posts, and versatile writing assignments.
- Claude 3.5 Sonnet: Excels at creative writing with “vibes.” My go-to for Instagram captions, emotional storytelling, and content requiring personality.
- Midjourney: Unmatched for visual content generation, providing creative assets to complement written content.
Understanding these distinctions has dramatically improved my workflow. For complex tasks, I sometimes use multiple models in sequence—Claude Opus for strategic thinking, followed by GPT-4o for execution, and Claude Sonnet for adding creative flair.
5. The Prompt That Works Today Might Not Work Tomorrow
The Problem: People become overly attached to specific prompt templates, failing to adapt when results decline or objectives change.
The Solution: Focus on the underlying process rather than the exact prompt wording.
AI models are constantly being updated, and what works perfectly today might underperform tomorrow. Instead of memorizing specific prompts, develop a problem-solving mindset.
Before prompting, I always:
- Define the exact output I need with concrete success criteria
- Imagine how a skilled human professional would approach this task
- Break down that professional process into discrete steps
- Design prompts that guide the AI through a similar workflow
This adaptable approach ensures I’m not caught off guard when a previously reliable prompt suddenly produces different results.
Bonus Principle: Design Your Prompts for Clarity
One thing I’ve learned that deserves special mention: clarity beats complexity every time.
When my prompts aren’t working, it’s often because I’m not being specific enough about what I want. Now I follow a simple template for most prompts:
CONTEXT: [Background information the AI needs to understand]
TASK: [Exactly what I need the AI to do]
FORMAT: [How I want the output structured]
TONE: [The emotional quality and personality of the content]
AUDIENCE: [Who will be consuming this content]
CONSTRAINTS: [Any limitations or things to avoid]
EXAMPLES: [Reference material showing what good looks like]This structured approach eliminates confusion and produces more consistent results.
Final Thoughts: Patience Pays Off
Mastering AI prompting isn’t an overnight achievement. It requires patience, persistence, and a willingness to experiment. There were many days when I felt frustrated by inconsistent results or seemingly inexplicable failures.
But the payoff has been transformative. What once would have required an entire content team now happens through strategic collaboration with AI tools. For small teams and solopreneurs, this capability isn’t just convenient—it’s revolutionary.
The most valuable mindset shift has been viewing AI not as a magic solution but as a sophisticated collaborative partner. Like any partnership, it requires clear communication, mutual understanding, and continuous improvement.
If you’re just starting your AI prompting journey, remember this: the learning curve may feel steep, but each interaction builds your skill. Before long, you’ll develop an intuitive sense for what works, turning AI from a occasional helper into your always-reliable partner-in-crime.
What prompting principles have you discovered in your work with AI? I’d love to hear your experiences in the comments below.
