Prompting with AI

The Biggest Lie About Prompting

The biggest lie in the AI space is that great results come from “the perfect prompt.”

They don’t.

Great results come from **prompting as a process**, not a sentence. Anyone selling “ultimate prompts” is selling confidence, not capability.

If prompting were about single messages, beginners would outperform experts. That doesn’t happen. Experts outperform beginners because they know how to *steer*, not just ask.

Prompting Is Iteration, Not Instruction

AI is not a command-line tool. You don’t issue one instruction and walk away.

Prompting is iterative:

• You give direction
• You evaluate output
• You correct errors
• You tighten constraints
• You repeat

This mirrors real collaboration. It’s the same way you’d work with a junior developer, writer, or analyst.

This is why understanding how AI behaves matters more than memorizing phrasing tricks.

Start With Intent, Not Output

Weak prompts start with “make this.” Strong prompts start with “here’s what I’m trying to accomplish.”

AI needs intent before it needs format. If you skip intent, the output drifts.

This is especially critical in SEO, where goals vary wildly: ranking, clarity, conversion, education, compliance.

You can see the difference between intent-driven work and generic output across my guides on on-page SEO, technical SEO, and schema implementation.

Constraints Are Power

The fastest way to improve AI output is to limit it.

Unlimited freedom produces generic responses. Constraints produce focus.

Strong constraints include:

• Audience definition
• Tone boundaries
• Structural rules
• Things to explicitly avoid
• Success criteria

This is why AI performs so well in systemized projects like tooling and SEO, where rules are explicit. You can see this applied in tools like the Quick SEO plugin and my custom Python tools.

The Follow-Up Prompt Is Where Real Work Happens

Beginners judge AI by the first response. Professionals judge it by the third or fourth.

Follow-up prompts are not corrections. They are refinements.

Examples of strong follow-ups:

• “This is too generic. Make it more opinionated.”
• “Remove repetition and tighten the argument.”
• “This section doesn’t support the main thesis. Rewrite it.”
• “Assume the reader already understands the basics.”

Notice what’s missing. There’s no magic language. Just judgment.

Prompting as a Conversation, Not a Form

AI responds best when treated like an ongoing conversation. Not because it has feelings — but because context compounds.

Each prompt builds on the last. Each correction sharpens alignment.

This is why one-off prompting fails. You reset context every time.

Long-running threads allow you to:

• Build shared assumptions
• Reference earlier decisions
• Maintain consistency
• Reduce repetition

Prompting Does Not Replace Editing

No amount of prompting replaces human editing.

AI can get you close. Editing gets you correct.

If you publish without editing, you are shipping drafts.

This is why AI-assisted work still requires accountability, especially in client-facing projects. It’s also why my Get Quote process remains human-led. AI assists. Humans approve.

Prompting Fails Without Domain Knowledge

The better you understand a field, the better your prompts become.

This is not optional.

You cannot prompt your way into expertise. You prompt *from* expertise.

This is why learning with AI must come before advanced prompting. Without understanding, prompts are guesses.

Why Prompt Libraries Don’t Scale

Prompt libraries feel useful because they reduce uncertainty. They don’t scale because they lack context.

The moment your project changes, the prompt breaks.

Effective prompting adapts. Libraries don’t.

Project-Level Prompting

The highest level of prompting happens at the project level. Not task-by-task.

This includes:

• Defining goals once
• Establishing tone and standards
• Referencing them repeatedly
• Enforcing consistency across outputs

This is how large systems stay coherent. This is how long-form content avoids contradiction. This is how tools feel intentional instead of stitched together.

Prompting Reflects Thinking Quality

AI does not hide sloppy thinking. It exposes it.

Vague prompts produce vague output. Conflicted prompts produce confused output.

Prompting well requires knowing what you want and why you want it.

How Prompting Connects the Entire AI Series

Prompting is the execution layer of everything else:

• Creating with AI defines collaboration
• Learning with AI builds expertise
• Limitations with AI defines boundaries
• Understanding with AI provides mental models

Without prompting, nothing moves. With bad prompting, everything degrades.

Prompting Is Responsibility

The faster you can produce output, the more responsibility you carry for its quality.

Prompting is not about getting more done. It’s about getting the *right* thing done.

The Truth About “Strong Prompts”

Strong prompts are not clever. They are clear.

They reflect understanding. They enforce standards. They evolve through feedback.

Anyone can copy a prompt. Very few people can steer a process.

This Is the Skill That Compounds

Prompting compounds because it improves everything else you do. Writing. Coding. Analysis. Strategy.

It’s not flashy. It’s not viral.

But it’s the difference between using AI and actually working with it.