Prompt engineering for marketing: how to write effective instructions for ChatGPT, Claude and Gemini

Anyone who has tried using an AI tool for work at least once has lived through this scene: you write a request, get a generic answer you can’t use, rewrite it, get something slightly better, try again. After ten minutes you could have written the text you wanted yourself.

The problem, almost always, isn’t the tool. It’s the prompt.

Prompt engineering is the practice of building precise instructions for language models. It isn’t just for developers: it’s a communication skill, and people who work in marketing already have half the kit they need. They know what they want to achieve, they know the audience, they have a goal. What’s often missing is knowing how to translate all this into an instruction the AI can really follow.

What prompt engineering means

A prompt is any text you give an AI model as input. It can be a question, a request, a brief, a set of rules. The way you build it determines the quality of the output much more than you might think.

Models like ChatGPT, Claude and Gemini don’t “understand” in the human sense of the word: they generate text based on statistical patterns. The more specific, contextualised and structured your prompt, the more the patterns the model activates are the right ones for your case.

A practical prompt engineering example: if you write “write me a LinkedIn post”, you get something neutral and presentable. If you write “write me a 150-word LinkedIn post aimed at marketing managers at manufacturing SMEs, in a direct and concrete tone, on lead generation with Google Ads, with a figure in the first paragraph and a closing question that invites comments”, you get something usable.

The difference isn’t in the length of the prompt. It’s in the quality of the information you give it.

The structure of an effective prompt

Every good marketing prompt contains these elements, to varying degrees:

• Role. Tell the model who it should be. “Act as a B2B copywriter specialising in SaaS” is more effective than “write a text”. The role steers the register, the vocabulary and the starting assumptions.
• Context. What’s happening, who the recipient is, what problem is being solved. Without context the model works in a vacuum. With the wrong context, it’s even worse.
• Task. Exactly what it has to produce: a text, an outline, a list, a revision. Being precise about the format helps.
• Constraints. Length, tone, keywords to include or exclude, things not to say. Constraints don’t limit the AI: they guide it towards the result you need.
• Example. If you have one, include it. Showing the kind of output you want is more effective than describing it in words.

How it works on ChatGPT, Claude and Gemini

The three main models react slightly differently to AI marketing instructions. Knowing these differences saves you time.

ChatGPT prompts for marketing

GPT-4 responds well to structured prompts with clear separators (dashes, distinct blocks). It tends to be verbose: if you want concise answers, say so explicitly. It’s very effective when you give it a specific role at the start of the conversation through the system message.

Claude prompts

Anthropic trained Claude with a different approach to safety and to interpreting instructions. Claude is particularly good at following complex, nuanced instructions, reasoning over long texts and respecting style constraints. If you work on detailed documents or briefs, Claude handles the context solidly. Its limitation: it’s sometimes more cautious than necessary, and you can get round this by clearly stating the professional purpose of the output.

Gemini prompts

Gemini has native web access and integrates with the Google ecosystem (Docs, Drive, Workspace). For anyone working on content that needs to take current events into account or that originates in Google environments, that’s a concrete advantage. It responds well to prompts with direct questions and handles multimodal context (text, images and data) more smoothly than the others.

Prompt engineering for specific marketing use cases

Copy for paid campaigns

The model needs: the target audience, the main benefit, any character limits (Google Ads: 30 characters per headline, 90 per description), the tone, and something that sets it apart from the competition. If you have examples of ads that worked in the past, attach them.

A prompt that works might be:

“Write 5 headlines for a Google Ads campaign aimed at HR managers at companies with 50-200 employees, for attendance management software. Each headline max 30 characters. Professional tone, no jargon. Focus on saving time.”

Blog content

For SEO content, the most useful prompts include the primary keyword, the secondary ones, the length, the audience and the search intent. You can also ask the model to build the outline first and approve it, then proceed section by section. This gives you much more control than asking for the whole article in one go. It’s the same logic as how to write effective prompts: breaking the task into steps produces better output.

Email marketing

Here the context counts double: where in the funnel the recipient is, what action you want them to take, what they already know about the product or brand. A prompt for a nurturing email to a lead who downloaded a white paper is completely different from one for a re-engagement email after 90 days of inactivity. If you’re building automated flows, prompt engineering ties in directly with the logic of email marketing automation: AI in modern workflows generates conditional copy variants based on user behaviour.

Common mistakes and how to avoid them

• Prompts that are too generic. “Write me something about our product” isn’t a prompt, it’s an abdication. The vaguer you are, the more generic the output.
• No context about the recipient. The model doesn’t know who you’re talking to. If you don’t tell it, it invents an average recipient who doesn’t exist.
• Not iterating. The first output is rarely the final one. Treat prompt engineering as a dialogue: take the result, identify what’s wrong and add a corrective instruction in the next message. In three or four steps you get where you want.
• Asking too much in a single prompt. If you need a complete article with outline, intro, body and CTA, proceed in stages. One prompt per section almost always produces better results than a single mega-prompt.

The prompt as the first step in a wider strategy

Prompt engineering isn’t an isolated trick. It’s the entry point for using AI as a real production tool, integrated into marketing processes. As we’ve already written in how to optimise content for AI, the quality of the input determines the quality of the output, and this applies both to the content you produce and to how it’s interpreted by generative search engines.

Anyone working on SEO knows that Google’s AI Overviews are changing the rules of organic visibility: building content with well-structured prompts lets you produce texts that answer the questions users ask more precisely, and that generative systems choose as a source.

The time you invest in improving your prompts is time that multiplies across every piece of content you produce. It isn’t a marginal optimisation.

If you want to understand how to integrate AI into your company’s marketing processes in a concrete and measurable way, the Digital Runner team is available for a call. Or keep reading the blog: you’ll find more articles on AI solutions and on the most practical digital marketing strategies for 2026.