Why Your Second AI Prompt Matters More Than Your First

The Concept: Learn to Refine the AI’s Output

We just finished the lessons on general prompting techniques. These are techniques you can use anytime you interact with AI.

Now we’re moving onto the second section of the course, which covers techniques that work best when chatting with the AI.

Depending on your experience level with AI prompting, you might be scratching your head asking, isn’t “chatting” with the AI the only type of prompting?

Would you be surprised if I said that it wasn’t?

Chatting is actually a more recent approach to interacting with AI, and it was pioneered by the company OpenAI through their ChatGPT interface.

The original approach to interacting with AI is what I call reusable prompting.

(As an aside, some people might try to call it one-shot prompting, but that gets confusing since it’s not a “shot” in the sense of few-shot prompting where you’d provide one or more examples. So the best name for this type of prompting is reusable prompting.)

What is reusable prompting?

It’s when you spend time creating a prompt that has the AI immediately deliver what you consider to be the “perfect” output. So there’s no need for a back-and-forth chat with the AI since it’s already generated what you consider to be the best output possible.

Implementing reusable prompting takes more time up front to create the prompt (and test it, tweak it, etc). But you save time later when you reuse the prompt, since you already have your perfect prompt and it doesn’t require a back-and-forth chat session.

So that’s reusable prompting. Now let’s distinguish it from chatting.

Chatting is when you type in a prompt, the AI generates a response, and then you tell the AI to modify its response somehow. For example, you might ask it to write more casually, remove common AI words, or rewrite it using a first person point of view.

You probably do this all the time in ChatGPT already, right?

What’s the benefit of chatting? Well, you can eventually get exactly what you want, keeping the back and forth chat going until you’re happy. It’s also great for learning new things.

What’s the downside of chatting? You haven’t created a reusable prompt; you’ve just created a chat history. So if you ever want to reuse the prompt, you have to start from scratch, have a long back-and-forth again, and you still might not get what you had before.

Now here’s the thing. You can use the prompting techniques I covered in the first part of the course anytime you interact with AI, whether you’re creating a reusable prompt or just chatting. But the prompting techniques I’m covering in this second part of the course? They only work while you’re chatting.

Make sense?

Okay, so let’s start really simple with the first lesson in this second part of the course.

In this lesson, I want you to understand one simple but important principle. When you’re chatting with AI, your second prompt matters more than your first prompt.

Let me show you how it works.

How it Works: Iterative Prompting

Another name for chatting with the AI is iterative prompting. This technique turns a simple question for the AI into a productive, collaborative dialogue, just like a real editorial process.

Here’s how it works:

Step 1: The Initial Assignment (Your First Prompt)

Start with a good, clear prompt to get a solid first draft from the AI.

The prompt doesn’t have to be complex. You can even base its foundation from my prompt blueprint in the previous lesson.

But again, it doesn’t have to be complex. The goal isn’t to make a reusable prompt. It’s simply to get a quick rough draft.

Step 2: Review the Draft

Analyze the AI’s response like an editor. What’s good? What’s weak? What’s missing? What sounds generic?

Step 3: Provide Specific, Actionable Feedback (Your Follow-up Prompts)

This is where the magic happens. Give the AI clear and direct instructions for improvement.

The key is: don’t be vague or provide weak feedback.

• Weak feedback: "Make it better."
• Strong feedback: "Remove all the generic phrases in the opening paragraph. Replace 'in today's digital landscape' with a specific statistic about cloud security breaches. Make it sound like a human expert wrote this, not an AI."

Step 4: Repeat the Loop

Continue this cycle of reviewing the AI’s new draft and providing further feedback until you have a final piece that meets your requirements.

Example of Iterative Prompting in Action

Let me show you how easy it is to implement this technique.

Imagine we need to generate a thought leadership article about cloud security for our company. I’ll set a 200 word limit for the sake of this example.

Write a thought leadership article (under 200 words) about the future of cloud security for B2B tech companies. Make it engaging and insightful for CTOs at mid-market companies.

The Future of Cloud Security for Mid-Market Innovators

For mid-market B2B tech companies, the cloud has become the default foundation for growth. But as infrastructure evolves, so do the threats. The next era of cloud security isn’t just about defense—it’s about resilience, intelligence, and trust.

CTOs can expect automation and AI-driven threat detection to move from “nice-to-have” to table stakes. Continuous verification, adaptive authentication, and real-time anomaly monitoring will define the new perimeter in a perimeter-less world. Cloud-native security tools will increasingly converge into unified platforms, reducing complexity while delivering deeper visibility across hybrid and multi-cloud environments.

What’s equally important is culture: security must shift from a specialized function to an embedded practice across engineering teams. As regulators sharpen compliance requirements, competitive advantage will hinge on demonstrating not just innovation, but provable security.

For mid-market leaders, the opportunity is clear. Those who embrace proactive, scalable, and transparent cloud security strategies will not only reduce risk—they’ll accelerate customer trust and market growth.

The future of cloud security isn’t a shield; it’s an enabler. The question for CTOs is no longer if you’ll adapt, but how fast.

Most prompting newbies stop right here. They’d hit publish and hope for a bazillion likes, thumbs up, and maybe even a call from an industry leader asking for advice on how their team hired so many flipping geniuses.

But in reality, those prompting newbies get… (crickets).

However, that’s not you!

You know this stuff reads like AI drivel.

It sounds professional but doesn’t say anything specific. There’s no unique insights, no real expertise, and zero personality.

P.S. It also screams “ChatGPT wrote this!”

So you decide to work your iterative prompting magic, and give the AI actionable advice on how to improve what it wrote:

Rewrite the opening with a specific problem CTOs face daily around cloud security. Make it visceral, not generic. Start with their biggest pain point.

The Future of Cloud Security for Mid-Market Innovators

Every CTO knows the sinking feeling: one misconfigured cloud setting, one overlooked API, and suddenly your entire business is exposed. In fast-moving environments, where engineering teams spin up new services by the hour, keeping security airtight feels less like strategy and more like whack-a-mole.

But the next era of cloud security isn’t just about plugging gaps—it’s about resilience, intelligence, and trust. Automation and AI-driven threat detection will move from “nice-to-have” to table stakes. Continuous verification, adaptive authentication, and real-time anomaly monitoring will define the new perimeter in a perimeter-less world. Unified, cloud-native security platforms will replace fragmented tools, cutting complexity while sharpening visibility across hybrid and multi-cloud ecosystems.

Equally vital is culture: security must evolve from a siloed function into an embedded practice within engineering. As regulators raise the bar, competitive advantage will depend not just on speed of innovation, but the ability to prove security at every layer.

For mid-market leaders, the path forward is clear. Proactive, scalable, transparent security won’t just reduce risk—it will accelerate trust and growth. The future of cloud security isn’t a shield; it’s a growth enabler. The question isn’t if you’ll adapt, but how fast.

Well, would you look at that!

It’s starting to sound a lot better.

The first paragraph hits on a common pain point for CTOs. It agitates that pain a bit, then provides a solution. That’s copywriting 101.

What else?

Can we make the output even better with some more iterative prompting?

Let’s try it!

Rewrite using the bucket brigade copywriting technique (but sparingly): shorter sentences (max 15 words), one idea per sentence, conversational transitions between paragraphs, break up technical lists, and add white space for better scannability.

The Future of Cloud Security for Mid-Market Innovators

One mistake.

That’s all it takes. A single misconfigured cloud setting, and your business is exposed.

CTOs live with this reality every day. Engineering teams move fast. Services spin up by the hour. Security often feels like an endless game of whack-a-mole.

So, what’s the way forward?

It’s not just about blocking threats anymore. It’s about resilience. Intelligence. Trust.

Here’s what’s coming:

– Automation and AI that spot attacks before humans can.
– Continuous verification and adaptive authentication with security that flexes in real time.
– Unified, cloud-native platforms that simplify tools and give clearer visibility across multi-cloud environments.

But tools alone aren’t enough.

Culture must change too. Security can’t stay siloed—it has to embed directly into engineering workflows. And as regulators tighten compliance, proving security will matter as much as delivering innovation.

Here’s the upside.

Mid-market leaders who lean into proactive, transparent security won’t just reduce risk. They’ll win trust. They’ll grow faster.

Because the future of cloud security isn’t a shield. It’s a growth engine.

The only question left: how fast will you adapt?

What do you think?

That’s a BIG difference!

Do see how easy it is to use iterative prompting? You start with something generic, then slowly dial it in to something incredible.

Now again, the downside I mentioned before is repeatability.

It took several steps to get to this result. We could theoretically compile a single prompt from this, but imagine if your chat lasted 30 minutes of tweaking. That would be a lot of back and forths to try and condense into a single unified prompt.

So keep this in mind when you start on your iterative prompting journey.

Advantages for Content Professionals

1. Transforms Generic into Genuine: The same prompt that produces AI-obvious content becomes the starting point for something authentic and valuable through iteration.
2. Maintains Control While Saving Time: You don’t need to craft the perfect initial prompt. Start simple, then guide the AI with your editorial expertise.
3. Builds Your Value, Not AI’s: The difference between the first draft and final version showcases YOUR editorial judgment and expertise, not the AI’s capabilities.
4. Creates Defensible Quality: When stakeholders question AI use, you can show them the progression from generic first draft to polished final, proving the value you added.

When to Use This Pattern

This iterative approach is useful whenever:

• Your reputation is on the line: Thought leadership, bylined articles, strategic documents
• The content needs authentic voice: Brand storytelling, executive communications, culture pieces
• You’re competing with human-written content: Guest posts, industry publications, award submissions
• Stakeholders are AI-skeptical: When your boss, clients, or readers value “human touch”
• The first draft feels generic: Anytime you read the AI’s response and think “this could be about anything”

The Bottom Line

The difference between amateur AI users and professionals isn’t in the first prompt. It’s in what happens next.

Amateurs accept the first draft. Professionals iterate until it’s exceptional.

Your second, third, and fourth prompts are where you transform AI output from obvious to outstanding, from generic to genius.

You’re not trying to hide that you used AI.

You’re using AI as a power tool for your editorial expertise.

The final product should showcase your judgment, your standards, and your ability to extract excellence from the AI.