What Is Prompting? Our Guide to Getting Started With Gen AI

Remember the good ol’ days when a prompt would at most remind you of payment?

Well, unless you’ve hidden in a mountain cave to conduct some extreme demographic research on hermits, you know that even kids use and learn prompting at a level that would make David Ogilvy’s head spin.

But there’s a qualitative difference between simple question answering — like asking Alexa for spicy omelette recipes — and getting an LLM to produce a blog post intro about the right marketing prompting procedure that also includes references to Ogilvy and hermits. Go ahead. Try it.

While these days, natural language processing is everywhere and there’s no doubt it’s capable of some incredible stunts, a prompt is not a prompt is not a prompt. To get the desired response, especially in content marketing, you as the prompteur (or promptigator?) need to keep honing new skill sets and help the AI with positive reinforcement. So that’s what we’ll look into today.

Why the Skill of Prompting Matters in the Age of Generative AI Models

In case you are sticking with trusted advertising recipes, possibly even writing copy by hand, because “that’s how Ogilvy did it” — we won’t judge.

There’s only one problem. Your competitors will use any tool they can get their hands on, and that includes the way they tweak their prompts for targeted advertising. When you ask them why, many will say they’re “unlocking hidden potentials.” But beyond the tech bro hype, you can actually expect some more concrete benefits of strategically controlling prompt inputs:

More accurate and relevant information: While AI hallucinations remain a problem, the right AI prompt is often more time-efficient than a Google search. Rather than type in hundreds of related queries, you can simply describe your exact context and let the bot handle prompting hierarchy.
Let the creative juices flow: Naturally, a verbal prompt like “come up with a brilliant business idea” won’t get you into prompt engineer heaven. What you can do, though, is use a model as a thinking partner or share a task direction by including a particular prompt dependency.
Use human and AI capabilities where they shine: Mental health advice? Maybe not. But there are plenty of repetitive tasks where humans are prone to errors anyway. Think data analysis or that one quarterly report you always rewrite in the same format.
Become a more effective AI user: The correct response is often just one explanation away. The more you learn about effectively prompting your AI tools, the better your results will be.

Types of Verbal Prompts: Different Approaches for Different Modes of Instruction

As in any field, it’s tough to improve what you can’t describe. If you’re entirely new to prompting, I’d suggest looking at it from as many angles as you can. That may even include applied behavior analysis, where you’ll often see gestural prompts or a target stimulus guide human learning. If you’re looking for the shortcut, here’s your vocab list to impress both your AI model and your neighbors.

Let’s say we’re working on an email campaign for a specialty coffee subscription service. Watch how the same basic task transforms as we level up our prompting game.

Zero-shot prompting is the beginner’s approach — you’re relying solely on the AI’s pre-existing knowledge with no examples.

Something like:

“Write an email about a specialty coffee subscription service.”

Sure, you’ll get something coffee-related, but it’ll be as generic as the office coffee your boss insists is “perfectly fine.” Zero-shot is great for simple tasks, but leaves a lot to chance.

With one-shot prompting, you’re giving the AI a single example to follow:

“Write an email about a specialty coffee subscription service. Here’s the tone I want: ‘Hey coffee lover! Life’s too short for boring beans. Ready to taste what you’ve been missing?’”

Now we’re getting somewhere — the AI has a style reference point and can mirror your enthusiasm.

Few-shot prompting takes this further by providing multiple examples, usually 2-5:

“Write an email about a specialty coffee subscription service based on the examples shared below.”

With these patterns, the AI can extract a consistent voice and structural approach.

For more fine-grained control, you can use instructional or completion prompts. So you’d either be handing the model all the parameters (email length, subject line keywords etc.) or the first few lines for a better understanding. It’s like having an intern who follows directions to the letter (except this one doesn’t raid your snack drawer).

Finally, role-playing prompts ask the AI to adopt a specific persona:

“You are a passionate third-generation coffee roaster who’s traveled to 27 countries sourcing the world’s most exceptional beans. Write an email to potential subscribers who are tired of ordinary coffee, explaining why your monthly subscription will transform their morning ritual.”

See the difference? Going from “write something about coffee” to crafting a prompt that embodies your brand voice can be the difference between an email that gets opened and one that gets relegated to the spam folder faster than you can say “instant coffee.”

Key Prompting Techniques and Styles (With Example Prompts)

Now that you’re familiar with the types of prompts, let’s talk about specific techniques that separate the AI whisperers from the casual users. Whether it’s text-only or a visual prompt paired with an explanation — think of this as your teaching style. After all, you’re trying to achieve a point where the model just “gets you,” and that doesn’t take one target skill but a whole range of options.

Direct instruction: “Generate a list of 5 pain points for small business owners struggling with inventory management.” Straightforward, to the point and effective when you know exactly what you need.

Socratic questioning: “What are the key challenges of inventory management for small businesses? How do these challenges affect their profitability? What solutions might address these issues most effectively?” This technique helps uncover insights AI might not produce with a direct command.

Creative collaboration: “I’m developing a SaaS tool for inventory management. Let’s explore some unique features that could set it apart. If you were a small business owner, what would make you switch from your current system?” This conversational approach often yields unexpected angles.

Constraint-based prompting sets clear boundaries: “Create a social media campaign for an inventory management app. The campaign should include 3 posts, each under 280 characters, use a casual but professional tone and incorporate inventory-related wordplay. Don’t mention competitors or use technical jargon.” The more specific your guardrails, the less likely you’ll need to course-correct later.

Beyond these styles, certain techniques consistently improve results:

Be specific and clear: Compare “Write content about coffee” with “Write a 300-word blog intro about specialty single-origin coffee beans, emphasizing flavor profiles and sustainable sourcing.” The difference is, well, clear.

Provide context that explains why you’re asking: “I’m creating content for mid-level managers who understand the basics of data analysis but struggle with implementation. Explain how predictive analytics can improve their decision-making process.” Context helps the AI tailor its response to your actual needs rather than a generic audience.

Specify the desired output format: Whether you need bullet points, a detailed table, JSON data or prose with the cadence of Hemingway, say so upfront. The AI won’t judge your specifications.

Chain-of-thought prompting encourages the AI to show its work: “Think step by step to calculate the ROI on this marketing campaign, considering both direct revenue and customer lifetime value.” This approach reduces errors and gives you insight into the AI’s reasoning process. Some advanced models, like Perplexity’s Deep Research option, also show their “thinking process” by default, but keep in mind that it disappears once you get the final output.

Break down complex tasks instead of attempting everything in one monster prompt: For a complex marketing strategy, you might first prompt for audience analysis, then messaging framework, then channel strategy and so on. Even if some models can handle complexity, breaking things down often produces more thoughtful results at each stage.

The Do’s and Don’ts of Effective Prompt Engineering

Like that colleague who somehow manages to get the best work from everyone in the office, effective prompt engineers follow certain principles. Here’s your cheat sheet:

Do:

Be precise and unambiguous. “Write a how-to guide for fixing a leaky faucet” beats “Tell me about plumbing.”
Provide sufficient context. Tell the AI who the content is for, why it matters and what background knowledge to assume.
Clearly define format and tone. Business formal? Conversational? Technical? Witty? The AI needs direction.
Use examples when needed. A snippet of the style you want can save paragraphs of explanation.
Assign roles or perspectives that frame the response. “As a sustainability expert…” focuses the lens.
Break down complexity into manageable chunks. Rome wasn’t built in one prompt.
Iterate and refine. Your first attempt probably won’t be perfect — and that’s fine.

Don’t:

Be overly vague or ambiguous. “Make something good” leaves too much to interpretation.
Assume the AI knows implicit context (though some models do build a picture of you over time).
Ask multiple unrelated questions in one prompt. Stay focused on a single goal.
Use unnecessarily complex sentence structures or jargon. No need to impress a bot. Clarity beats cleverness.
Forget to specify constraints like length, style or format. Otherwise, prepare for a surprise.
Give up after the first try! Prompting is a conversation, not a one-and-done.

Modifying AI Prompts for Desired Behavior: The Art of Iteration

Even the best prompt engineers rarely nail it on the first try. The secret to excellence lies in systematic refinement:

Analyze the output. Did the AI misunderstand your request? Was the format wrong? Did it sound like a corporate press release when you wanted conversational blog content? Identify the gap between what you received and what you wanted.

Identify what was missing in your original prompt. Was it lacking specificity? Context? An example? Clear constraints? This diagnosis guides your next attempt.

Refine systematically by:

Adding more detail or constraints: “Include statistics from the last 5 years” or “Focus on beginner-friendly explanations.”
Rephrasing with clearer language: “Generate a comparison table” instead of “Tell me about the differences.”
Providing an example that demonstrates the style, format or depth you want.
Trying a different role assignment: “As a product designer” might yield different insights than “As a marketer.”
Simplifying the task or breaking it into sequential prompts.

Remember that different phrasings can produce dramatically different results. The AI isn’t trying to be difficult or sassy — it’s responding to subtle cues in your language that you might not even realize you’re providing.

Prompting as a Skill for the Future

In the not-so-distant past, learning HTML felt optional for marketers. Now, basic coding knowledge is practically a prerequisite. Similarly, effective prompting is quickly becoming the dividing line between those who merely use AI tools and those who leverage them to their full potential.

The good news? Unlike some technical skills that require months of study, prompting improves with deliberate practice and experimentation. Each interaction teaches you something about how these models respond, what triggers better outputs and which techniques work for specific tasks.

The most successful prompt engineers aren’t necessarily those with computer science degrees — they’re the curious users who treat each prompt as a mini-experiment, refining their approach based on results.

So start practicing. Yes, it might feel odd to “practice talking to a computer,” but your competitors are already doing it. Test different approaches, save your best prompts and build a personal library of techniques that consistently deliver. Your future self (and your impressed colleagues) will thank you.

Note: This article was originally published on contentmarketing.ai.