The best AI results I've gotten didn't come from better prompts. They came from treating Claude and ChatGPT the way I'd treat a brilliant new hire on day one: someone with real expertise, but zero context about my business, my standards, or my voice.
Most content creators keep hunting for the magical prompt that will finally produce polished output on the first try. They're solving the wrong problem.
The Vending Machine Mistake
Here's what I've noticed after working with AI tools for the past two years: most people treat AI like a vending machine.
Insert prompt. Receive output. Complain that the output is generic.
But think about what you're actually expecting. You're asking a system that knows nothing about your audience, your voice, your past work, or your specific standards to produce something that sounds exactly like you wrote it.
That's not a reasonable expectation of any collaborator. Not a freelancer. Not an employee. Not anyone.
The New Hire Frame
Let me show you what I mean. Imagine you just hired someone brilliant. They have ten years of experience in content strategy. They've worked with major brands. Their portfolio is impressive.
Day one, you hand them a brief: “Write me a blog post about productivity systems.”
What would you expect?
If you're honest, you'd expect a competent first draft that misses your voice entirely. You'd expect them to nail the structure but choose examples you wouldn't have chosen. You'd expect to spend an hour on feedback and revisions before it felt like something you'd actually publish.
Nobody would expect a perfect final draft from a new hire on day one. But that's exactly what we expect from AI.
Here's the thing: AI has the same problem your new hire does. It has expertise. It has capability. What it doesn't have is context about you.
The Three Parallels
Let me break this down into three specific ways AI is exactly like onboarding a new team member.
1. They don't know your company
Your new hire doesn't know that you never use bullet points in blog posts. They don't know your audience is sophisticated and hates being talked down to. They don't know you have a strong opinion about semicolons.
AI doesn't know any of this either. The first output is going to miss all of it. But if you provide feedback, both the hire and the AI can learn.
The fix isn't finding a new hire. The fix is providing context and correction.
2. They need feedback loops, not perfection pressure
When your new hire submits that first draft, you don't fire them for getting it wrong. You give feedback. “This section is too formal. Can you make it more conversational? The example in paragraph three doesn't land. Try something more specific.”
This is exactly how AI improves. Not through a single perfect prompt, but through iteration. You give feedback. The output improves. You give more feedback. It improves again.
Successive approximation. Getting closer and closer to what you want.
Most people never get to the third iteration. They give up after the first output doesn't match their vision. But the first output was never going to match. That's not how collaboration works.
3. They get better over time with direction
Three months in, your new hire starts anticipating what you want. They've internalized your voice. They know which clients to reference and which to avoid. They can draft something that needs minimal editing.
AI can work the same way, especially tools like Claude Projects or custom GPTs where you can store context. The more direction you provide over time, the more the outputs align with your standards.
But you have to invest in the relationship. You have to teach. You have to iterate.
What Successive Approximation Actually Looks Like
Here's what I do instead of one-shotting prompts:
I start with discovery. “Please review this data and look for patterns I might miss.” I'm not looking for the answer. I'm looking for what I haven't seen yet.
Then I probe. “That third pattern is interesting. What else connects to it?” Now I'm getting closer to something useful.
Then I challenge. “I'm not sure about that conclusion. What evidence contradicts it?” I want the AI to stress-test its own thinking.
Then I apply. “Based on what we've found, draft a summary of the three most actionable insights.” Now I have something to react to.
Then I refine. “This is close but the second insight feels weak. Can you strengthen the evidence or suggest an alternative?” Getting closer.
This process takes longer than a single prompt. But here's the paradox: it's actually faster than starting over three times because your one-shot prompts keep failing.
The Shift You Need to Make
The mental model matters more than the specific prompts.
Stop thinking “prompt engineering.” Start thinking “collaboration engineering.”
You're not programming a machine. You're onboarding a colleague who happens to work at the speed of light but has zero institutional knowledge.
What would you do with that colleague? You'd invest time upfront. You'd provide context documents. You'd give feedback on early work. You'd build shared understanding over time.
That's exactly what works with AI.
One Change to Try Today
Next time you use Claude or ChatGPT, try this: before asking for the final output, ask for observations about your problem first. Ask what patterns it sees. Ask what you might be missing.
Pick the insight that resonates. Then ask for an expansion of just that angle. Then ask for a draft of one section.
Build toward the final output through iteration, not a single leap.
You'll get better results. You'll understand AI's strengths and limitations more clearly. And you'll stop being disappointed by outputs that were never going to be perfect on the first try.
The magic isn't in the prompt. It's in the process.