How I Design Prompts Using Google’s Agent Platform
A practical framework for briefing AI like a team member and getting work you can actually use.I studied Google’s official framework, translated into plain language with real campaign examples.Most marketers treat AI like a search engine. They type a question and hope for a useful answer. That…
A practical framework for briefing AI like a team member and getting work you can actually use.I studied Google’s official framework, translated into plain language with real campaign examples.Most marketers treat AI like a search engine. They type a question and hope for a useful answer. That works sometimes. But it leaves most of the model’s capability untouched.When I started studying Google’s official prompt design framework for their Agent Platform, something clicked. The framework was written for developers. But the thinking behind it was pure marketing strategy. Brief your model the way you brief a team member, and the output changes completely.I translated that framework into something a marketer can use. Here is what I learned and how I apply it in real campaigns.What Prompt Design Actually IsGoogle defines prompt design as the process of creating prompts that elicit the desired response from language models. Updating prompts and assessing the model’s responses is called prompt engineering.In plain language, it means this. How you talk to an AI model determines what it gives you back. A vague brief produces a generic response. A structured brief produces something usable.Google’s framework breaks every effective prompt into four components. Task, System Instructions, Few Shot Examples, and Contextual Information. Each one serves a different purpose. Together they give the model everything it needs to produce accurate, on-brand output.Component One — The TaskThe task is the core request. What do you want the model to do?Google breaks tasks into two types. A question task asks the model for information. An instruction task tells the model to produce something specific.Most marketers default to question tasks without realising it. “What should I write for this campaign?” is a question. The model gives you an opinion. “Write five Instagram captions for this product launch targeting millennial homeowners in the UK, under 150 characters each, ending with a call to action” is an instruction.The model gives you something usable. That difference sounds small. The output difference is not.For a home services client campaign I ran, the task I gave Gemini was this: “Write three email subject lines for a re-engagement campaign targeting homeowners who requested a quote but did not convert. Tone is warm and direct. Each subject line under 50 characters. No clickbait.”Specific task. Defined format. Clear constraints. The model had nothing to guess.The task alone can produce decent output. But without the other three components, it is still working with incomplete information. That is where the rest of the framework matters.Component Two — System InstructionsSystem instructions are the rules and persona you set before any conversation begins. They persist across the entire interaction without needing to be repeated in every prompt.Think of them as the briefing you give a new team member on their first day. This is who we are. This is how we talk. These are the things we never do.Here is a real example from a campaign I worked on for an eco-friendly cleaning brand:“Act as a witty, direct-to-consumer brand strategist for an eco-friendly cleaning company, maintaining a playful tone while ensuring all product claims adhere to strict Green Guides compliance.”That single instruction does four things at once. It sets the role. It defines the tone. It establishes the audience. And it adds a compliance guardrail that protects the brand from making claims it cannot legally back up.Without that instruction, the model might produce technically accurate content that sounds wrong for the brand. With it, every output starts from the right place.System instructions are most valuable in multi-turn conversations and agent workflows where you need consistent behaviour across many interactions without having to repeat yourself.Component Three — Few Shot ExamplesFew-shot examples are the most underused component in marketing prompts.Instead of telling the model what you want, you show it. Provide two or three examples of the kind of output you need, and let the model mirror the pattern.This is powerful for social media content where tone, cadence, and emoji placement are part of what makes a post convert.For a client’s Instagram campaigns, I included three of their top-performing captions paired with their corresponding sales hooks as reference templates before asking the model to generate new posts. I did not describe the style. I showed it.The output matched the brand voice more closely than any instruction-based prompt I had used before. The model picked up on sentence rhythm, the placement of the call to action, and the specific emoji combinations that had driven the highest engagement in previous campaigns.Few-shot examples also work well for classification tasks. If you need the model to categorise customer feedback, label content types, or sort leads by intent, showing it three or four correct examples produces far more accurate results than describing the categories in words.Component Four — Contextual InformationContextual information is the raw data, facts, and documents you feed the model to ground its output in reality.This is the component that prevents hallucination. When a model does not have specific information it needs, it fills the gap with a plausible-sounding guess. In marketing, that guess can be damaging.The solution is simple. Give the model the facts before asking it to write.For a product launch email series, I included the full Q3 launch brief, the target audience demographics, and a competitor pricing comparison table directly in the prompt. The model did not need to guess at positioning, audience pain points, or competitive advantages. It had all of it.The output was a facts-based email nurture sequence that referenced specific product benefits, spoke to the right audience concerns, and positioned the product accurately against competitors. None of that would have been possible with a generic prompt.Contextual information is also what makes AI useful for private or proprietary work. The model was not trained on your client’s data. But you can give it that data at the moment of the prompt, and it will reason from it accurately.How These Four Components Work TogetherThe task defines what you want. System instructions define who the model should be and how it should behave. Few-shot examples show what good output looks like. Contextual information gives the model the facts it needs to get the details right.Use all four, and you have given the model everything a skilled team member would need to complete the work well.Here is what that looks like as a complete prompt structure for a marketing campaign:System instruction sets the brand voice and compliance rules. Contextual information provides the product details, audience data, and competitive landscape. Few-shot examples show three high-performing pieces of content from previous campaigns. The task asks for five new variations of the lead email in the nurture sequence.That brief takes five minutes to write. The output would take a copywriter half a day.What I LearnedPrompt design is not a technical skill. It is a communication skill that marketers already have.Briefing a model is the same as briefing a freelancer, a designer, or an agency. The clearer and more structured your brief, the better the work that comes back.Google’s framework gave me a way to structure that brief consistently. System instructions replaced the vague “write in our brand voice” instruction that never quite worked. Few-shot examples replaced the long style guide nobody reads. Contextual information replaced the assumption that the model already knows your client’s business.The framework was written for developers. But the thinking behind it is something every marketer already understands. You brief people for a living. Now you can brief AI the same way.What part of your current AI prompting process do you think is weakest?Drop it in the comments, and I will tell you which component is missing.This story is published on Generative AI. Connect with us on LinkedIn and follow Zeniteq to stay in the loop with the latest AI stories.Subscribe to our newsletter and YouTube channel to stay updated with the latest news and updates on generative AI. Let’s shape the future of AI together!How I Design Prompts Using Google’s Agent Platform was originally published in Generative AI on Medium, where people are continuing the conversation by highlighting and responding to this story.Source: Generative AI Pub — Published — Category: Image AI