Prompt engineering: The secret to creating better content

This article is based on Ryan Oistacher’s brilliant talk at the AI for Marketers Summit. RMA members can enjoy the complete recording here.

AI adoption is skyrocketing, and it’s reshaping the way we work. But for all its benefits, AI still has a reputation problem. 

People often complain that its outputs are too vague, unreliable, or bland. And let’s be honest – many of us struggle to admit when AI does something faster or more efficiently than we can.

The truth? AI is only as good as the prompts we give it. If you’re not getting high-quality results, the issue might not be the model – it might be how you’re asking it to work.

That’s where prompt engineering comes in. Mastering the art of writing precise, well-structured prompts can help you get more useful, targeted, and high-quality AI-generated content.

So, in this article, we’ll explore:

• The key components of an effective prompt.
• Techniques to refine and improve AI outputs.
• Real-world examples of AI-driven content creation.
• Resources to help you take your AI skills to the next level.

If you want to harness AI’s full potential, it all starts with better prompts. Let’s dive in.

AI is taking over the workplace

AI adoption is exploding in the workplace. According to the 2024 Work Trend Index report, published by Microsoft and LinkedIn: 

• 75% of respondents use AI at work.
• 46% of them started using AI less than six months ago.
• 90% of AI users say it helps them save time, focus better, and be more creative.

With all these productivity and creativity gains, it’s no surprise that AI skills are in demand. Hiring managers are actively looking for employees with AI expertise. In my opinion, there’s never been a better time to be a marketing strategist with AI experience.

At the same time, there’s growing concern about AI’s impact on jobs. The reality is that some roles will inevitably change – copywriters, data analysts, content marketers, and SEO specialists are already seeing shifts in their responsibilities. 

Sam Altman, CEO of OpenAI, even stated that AI will replace 95% of creative marketing work. That’s a pretty scary proposition, and it’s a recurring theme in discussions about AI.

AI as a tool, not a replacement

Despite the concerns, marketing requires a high level of strategy, creativity, and decision-making – things AI can’t fully replicate. In fact, AI and large language models (LLMs) will likely help us the most by removing friction between us and subject matter experts or siloed teams.

If AI helps us centralize work – so we don’t have to wait two weeks for the SEO team to write copy or for PR to draft a press release – we can move faster and be more effective. And all of that can start with the power of a well-crafted prompt.

What is prompt engineering?

So, what exactly is prompt engineering? It’s the process of crafting the right prompts to get the best and most accurate AI-generated output possible.

At Netwrix, I’ve built prompts for everything from persona development and positioning documents to competitive battlecards, sales guides, call scripts, and demo scripts. Honestly, I use AI for almost everything I do.

Especially in a private equity-backed company like mine, where efficiency is critical and margins matter, AI is a game-changer. The acquisitions aren’t slowing down. The work isn’t slowing down. The sales demands aren’t slowing down.

We need a scalable way to create content across different verticals, use cases, and functional roles. AI helps us do exactly that. And at the core of making AI work effectively? It all comes down to prompt engineering. The better the prompt, the better the output – so mastering this skill is key to unlocking AI’s full potential.

The 7 key components of a strong prompt

A prompt can include many different components, but after refining a wide range of marketing deliverables, I’ve found that these seven elements are the most important.

1. Task: Define what you want the AI to do

First and foremost, you need to define the task. This is the primary output you're expecting from the AI – whether it’s generating a list, writing a blog post, rewriting content, or analyzing data.

Personally, I tend to be a bit verbose in my writing, so I often ask AI to make my content clearer, more concise, and easier to read. Being specific about the task helps ensure you get exactly what you need.

2. Context: Provide background information

Next, context is crucial. This could include background details, unique selling points, market conditions, or business challenges. The more context you provide, the better the AI will understand what you need.

You can feed entire product documentation sets into an AI model. Since models like Claude have a 200,000-token context window, they can reference extensive documentation to generate highly relevant and accurate responses.

One of the biggest complaints I hear is that AI-generated content can feel generic or even inaccurate. If you’re getting generic or off-base responses, try adding more context. Push as much relevant information as possible into your prompt, then refine your approach based on the output.

3. Example: Show what good looks like

Examples are a game-changer in prompt engineering. They serve multiple purposes:

• Clarifying what you're asking AI to produce.
• Reducing ambiguity in the task.
• Helping the model mimic the tone, style, and structure you want.

People often worry that AI-generated content feels inauthentic. A simple way to improve this is to feed the model examples of high-quality, authentic content – articles, blog posts, or case studies you're proud of. You’ll be surprised by how much better the output becomes when the AI has a strong reference point.