Stop writing PRDs for AI — start using prompt sets instead

UX designers frequently work in ambiguous spaces, most notably the discovery phase. We collaborate closely with product managers to identify new problems, understand users’ goals and frustrations, and strategically develop solutions to address their needs. However, the best solutions aren’t always straightforward, and with AI being embedded in every new product and feature, it makes things a bit more challenging. Just as we get comfortable using AI, something changes or evolves. This makes AI features unpredictable and difficult to document requirements for.

Traditional product requirements documents (PRDs) outline the intended purpose, functionality, and behavior of a product, serving as a guideline for development teams and stakeholders. However, the ambiguity with AI’s behavior can create misalignment across the team and potentially a disastrous outcome.

To mitigate this risk, designers should adopt an iterative approach using prompt sets to define exactly how AI should behave and respond across different scenarios. Just as we ensure consistency in visual patterns and copy, we also shape how users interact with a product. This leads to how they will ultimately feel while using it.

The same applies to AI features, as designers play a key role in determining whether outputs align with the brand tone of voice, feel trustworthy, and provide genuine value. While engineers and PMs focus on feasibility and business rules, designers are uniquely positioned to own prompt sets, just as we do with our design files.

Let’s talk about why AI requirements need real interaction to become clear, how prompt-set prototypes reveal what works faster than documents, and how early testing aligns teams. You’ll see how prompt sets act as detailed use cases that enable quick iteration and foster collaboration across design, product, and engineering to clarify goals and deliver value efficiently.

What are prompt sets?

First, let’s talk about what a prompt set is. They are essentially a collection of curated written prompts paired with their expected responses, tone, and structure. They help define how an AI system should behave in specific scenarios and ensure that all use cases are covered. In a way, a prompt set is similar to a film script, which outlines the dialogue and execution details like stage directions.

For example, a customer support product may feature an AI assistant that helps support staff manage and resolve their tickets.

In a PRD, a requirement might be written “The AI should summarize support tickets.” Different team members may interpret this differently. Should the AI condense tickets into one sentence? Or should it generate a structured report? Not having specifics can slow down alignment and risk wasted effort.

A prompt set consists of a prompt and details of the expected response, like the tone, structure, and constraints of the output.

• Prompt — “Summarize the customer’s last three support tickets in plain language”
• Expected response — “Here’s a summary of your last three tickets: …” delivered in a friendly, concise tone, organized as bullet points, and avoiding technical jargon

This functions as both a design spec and a test case for AI behavior, removing ambiguity and aligning the team on what a good outcome looks like. Instead of debating abstract requirements, teams can see exactly how the AI should respond and quickly spot gaps or issues. Sometimes, AI doesn’t respond the way we expect, so refining the prompt to be more direct or specific can help generate the proper response.

Using prompt sets to prototype and test early

How can designers use prompt sets in their design process? When it comes to building AI products, speed to market is essential for gaining market share over your competitors. Building features quickly and testing them with real customers allows you to gather feedback and refine your product as soon as possible.

Prompt sets make this early testing far easier than relying on lengthy product requirement documents. Prototyping real AI interactions can demonstrate outcomes that may be missed by requirements specs, ensuring that all edge cases are accounted for. Oftentimes, AI can produce responses that were not thought of or generate unexpected outputs. It can be difficult to define requirements without actually testing a prototype of the AI feature first. Also, having concrete examples of prompts and responses makes it easier for teams to align around and test out.

By testing out real prompts and expected responses, teams can see exactly how an AI feature behaves before committing to code. This shared reference point helps designers spot UX issues, PMs validate the vision, and engineers flag feasibility concerns early. With AI, even small changes to words or phrasing can dramatically shift results, which is why showing a working example almost always beats writing a long requirements document.

How to create and use prompt sets

Here’s a step-by-step process for creating prompt sets and integrating them into your design workflow. This should be a collaborative effort between designers, product managers, and developers so the entire team can use, refine, and improve them over time.

Define use cases

Start by working closely with your product manager to identify the core scenarios your AI feature will handle. Focus on the most common and high-impact interactions. Depending on the context, some examples could include onboarding, troubleshooting, summarizing, or creative generation.

A great way to surface these is through a user journey workshop, where the team maps out every possible interaction a user might have with the feature. This helps you visualize gaps and spot overlooked scenarios. For a comprehensive overview of your user’s journey, make sure to account for edge cases, such as when the user requests something the AI can’t do, or when errors occur, so your prompts cover both ideal and failure states.

Write prompts for each scenario

Once you’ve mapped out your user journeys, it’s time to draft your prompts for each scenario. When writing prompts, you should keep them specific and contextual, as the AI’s response will be based on the context that you give it. The more context you include in your prompt, the more specific and potentially helpful the response will be.

When describing the expected response, make sure to include the tone of voice that it has (typically based on your brand guidelines). You should also specify how the response should be formatted, whether you want a bullet point list, step-by-step instructions, a paragraph, or an email draft. Don’t forget to include any constraints, such as avoiding technical jargon, limiting word count, or ensuring inclusivity.

Here’s an example of an effective prompt set. Let’s say our AI assistant can help users troubleshoot problems with logging in.

Prompt set: “A user can’t log in to their account. Respond in a friendly, reassuring tone. Provide a clear step-by-step troubleshooting guide (max 5 steps) written at a non-technical reading level. If the issue might require contacting support, end with: ‘If these steps don’t work, our support team is happy to help.’”

This prompt is effective for many reasons. It gives the AI context about the problem, which is that the user can’t log in. It specifies the tone of voice, defines the structure of the response, and sets constraints. Including guardrails in the prompt set limits the chances of the AI surprising your users with an unexpected response.

Test and iterate in real time

Of course, prompts are never perfect to begin with. This is where testing them multiple times will help you refine and iterate your prompts to output consistent and relevant responses. You can either use real or simulated inputs, but the key is to test your prompts continuously and flag any bad responses. Involve your PMs, designers, and developers during the review process to ensure that the output is as expected and gather feedback on how the prompt can be improved.

As you refine your prompt sets, test them out with multiple users to get their perspective on how helpful the responses are to them. Make sure to document their feedback and incorporate their suggestions into your next iteration. Watch out for small wording changes as they can drastically alter the AI’s outputs.

Integrating prompt sets into your design system

As you build out your library of prompt sets, treat it like a design system library. Store them in a shared document or design system asset so the entire team has quick, consistent access. This enables cross-functional teams to review, test, and refine prompt sets together.

Organize your prompt sets by use case or stage of the user journey, and give them a clear descriptive name, like “Support Summary Prompt”. This makes it easier for stakeholders to find prompts tied to a specific action or customer request. Just like a UI component, provide usage guidelines so team members know when and how to use it. Don’t forget to document the expected behavior to set standards for quality and consistency.

Each prompt set should also be version-controlled, maintaining a clear history so teams can review changes and roll back if the current version isn’t performing well. Assign an owner to each prompt set, who is responsible for maintaining it. The owner should have final approval of any change requests to the prompt sets to ensure the prompt remains effective.

Example workflow

To ensure smooth collaboration, many teams adopt a simple workflow:

1. Owner drafts the initial prompt set, describing the tone, structure, and user value.
2. PM reviews for alignment with business goals and edge cases.
3. Engineer tests the prompt in the system, flagging feasibility or performance issues.
4. Team review happens asynchronously or live, with feedback logged directly in the doc.
5. Owner iterates on the prompt set based on feedback.
6. Steps 2 to 5 repeat until the prompt meets requirements.
7. Owner approves the final version.

This process mirrors the design process itself, as production work is drafted, reviewed, and iterated on until it meets the requirements.

As your prompt set library grows over time, encourage designers, PMs, engineers, and support teams to log issues or suggestions when a prompt doesn’t perform well. You can create a regular review cadence to evaluate and discuss how the most-used prompts can be improved. Make reviewing the prompts a cross-functional exercise, whether part of a design critique session or sprint reviews.

The value of using prompt sets

Before using prompt sets, a requirements document might simply state something like, “AI should summarize tickets,” leaving engineers to interpret the instruction on their own. This often leads to misaligned outputs and extra iterations.

With prompt sets, the exact prompt and a sample output are documented upfront, ensuring everyone is aligned on expectations before any code is written. This not only reduces ambiguity but also allows teams to prototype AI features quickly, test responses early, and iterate on design and tone before integrating the model into the product.

By treating prompt sets as a lightweight prototyping tool, teams can validate AI behavior, refine user experiences, and foster cross-functional collaboration, ensuring designers, PMs, and engineers are all aligned on both functionality and user-facing outcomes. Success can be measured with clear UX metrics, such as task completion rate, time-to-resolution, reduction in errors, and user satisfaction scores. This way, teams not only prototype faster, but also know when a prompt set is delivering real value and can continuously work to improve it.

Conclusion

Prompt sets turn abstract AI feature ideas into concrete, testable behaviors. By documenting exact prompts, expected outputs, and usage guidelines, teams can prototype AI interactions quickly, validate responses early, and iterate on tone and structure before any code is written. This reduces misalignment, speeds up development, and ensures a consistent, high-quality user experience.

Prompt sets also act as a shared, version-controlled library, enabling reuse across features and products. They foster cross-functional collaboration and create a clear feedback loop for continuous improvement. Designers, product managers, and engineers can all reference the same prompts, reducing ambiguity and supporting better decision-making throughout the design and development workflow.

If you’re currently designing generative AI products, start building a prompt set for your next prototype. Even a small set of well-documented prompts can improve clarity, alignment, and efficiency, while laying the foundation for scalable, user-focused AI development.