Design prompt-building interfaces

How to help users articulate their intents strategically

Screenshot of an AI design tool showing image generation settings on the left and a chat prompt for AI-assisted UX research on the right.

One of the most persistent problems in AI products today is this: people still aren’t sure what these systems are capable of, or how to get the best out of them.

This is because most AI tools still greet users with a single blank box and a placeholder as vague as it is open-ended: “Ask anything.”

The result? Without clear guidance, users start crafting prompts in haste, iterate through endless revisions, lose control of the flow, and gradually pollute the context with fragmented instructions.

Screenshot of a chat interface showing the message “Hey, Sen. What can I help with?” above a search-style input bar labeled “Ask anything.”
Example of the default chat box in most AI products, yes, you can ask anything.

To improve the quality of AI output, clarity must start at the very beginning — in the way we express intent. A good prompt isn’t just a line of text, it’s the source code of collaboration.

That’s why we need prompt-building interfaces that are designed not merely to receive instructions, but to shape them. They help users understand the product’s potential while progressively clarifying their own goals through interaction.

This article looks at how we can design such interfaces through real examples and prototyping. From those explorations, I’ve distilled three design strategies that shape an effective prompt-building experience:

Graphic titled “Prompt-Building Interface Design Strategy” showing three strategies: Guide (with a lightbulb icon), Constrain (with a vault icon), and Structure (with a web layout icon).
  1. Guide — enable users to start the conversation and express intent clearly.
  2. Constrain — set meaningful boundaries to narrow focus and maintain coherence.
  3. Structure — provide a compositional logic that organizes dialogue and outcome.
Illustration labeled “Strategy 1: Guide” with a glowing blue lightbulb and the text “Enable users to start the conversation and express intent clearly.”

Turn the qualities of a good prompt into measurable, actionable criteria, and let AI use those criteria to refine the user’s expression.

Across the industry, there’s now a rough consensus on what makes a good prompt. Anthropic, for instance, recommends using XML tags to structure prompts — breaking them down into roles, tasks, constraints, and examples as modular components.

You’re a financial analyst at AcmeCorp. Generate a Q2 financial report for our investors.

AcmeCorp is a B2B SaaS company. Our investors value transparency and actionable insights.

Use this data for your report:<data>{{SPREADSHEET_DATA}}</data>

<instructions>
1. Include sections: Revenue Growth, Profit Margins, Cash Flow.
2. Highlight strengths and areas for improvement.
</instructions>

Make your tone concise and professional. Follow this structure:
<formatting_example>{{Q1_REPORT}}</formatting_example>

While this format may feel intimidating for most users, it reveals something important:

the attributes of a good prompt are not mystical; they can be analyzed, formalized, and embedded directly into the product itself.

Guide means leveraging AI to help users refine natural and unstructured input into expressions that are clear, consistent, and reproducible.

Feature

  • Evaluator: Reviews the prompt, rates its quality, and highlights areas for improvement.
  • Enhancer: Offers several optimized rewrites, allowing one-click reconstruction with highlighted differences.

Example

Screenshot of a tool called “Elicit” showing how research questions about generative UI are evaluated and refined. The left side labeled “Evaluator” assesses question quality, while the right side labeled “Enhancer” suggests clearer, more specific research questions.

Elicit is one of the few products that combine both evaluation and enhancement of prompt.

It assesses the quality of a user’s research question and dynamically proposes refinement paths, such as defining methods or narrowing focus areas. This makes it particularly suitable for academic research.

Screenshot of “Monica AI — One Tab Prompt Optimization” showing how a basic prompt about Asian consumer trends is expanded into a detailed report outline. The example highlights how allowing users to choose optimization aspects maintains control and improves prompt refinement.

Monica AI, by contrast, offers a simple one-click optimization. It is simple and accessible but lacks multiple refinement options, focusing instead on convenience and speed.

Prototype

I explored how prompt guiding could offer richer options while giving users greater control. The focus was to understand how design can mediate the balance between the generative freedom of AI and the editorial control of users.

Animated demo showing a user typing a prompt into an AI interface. The system automatically evaluates the prompt’s strength and suggests several enhancement options such as “More structural” and “Encourage reasoning.” The user selects one option, and the improved version smoothly replaces the original prompt.
Visual walkthrough of a prompt enhancement flow showing three stages: evaluating a prompt, selecting and editing an improved version, and applying changes to produce a refined prompt for AI chat interface analysis.
Illustration labeled “Strategy 2: Constrain” with an open vault icon and the text “Set meaningful boundaries to narrow focus and maintain coherence.”

Define the informational scope to create clear boundaries for interaction, ensuring that the generated results remain focused and controllable.

Constrain addresses the question of how to clarify the topic and the limits of discussion. The key is to introduce contextual constraints that keep the model operating within a defined set of materials, time periods, or domains.

By limiting AI’s visible scope, it prevents drift and vagueness, resulting in outputs that are more focused and reliable.

Feature

  • Boundary filter: For tasks involving web search or information retrieval, the system can dynamically generate filters aligned with the user’s query, such as time range, source type, or domain category. This allows users to deliberately define the boundaries of the response.

Example

Screenshot of the Consensus research interface showing how users can set filters while prompting, with options for publication year, journal rank, methodology, fields of study, and countries.

Consensus provides global filtering capabilities that let users specify parameters like publication year, research method, knowledge domain, or geographic region at the prompt stage.

This ensures that the model’s reasoning remains grounded within a well-defined and transparent scope.

Screenshot of Scite’s AI research assistant showing the “Settings” menu and a pop-up for adjusting assistant settings, including reference usage, evidence sources, citation style, and response length.

Similarly, Scite includes assistant settings where users can define academic parameters before starting the query. It suggests that scientific research tools tend to offer more control upfront.

Screenshot of Booking.com’s search interface highlighting the “Smart filters” feature, where users can describe what they’re looking for in plain language to refine hotel search results.

Booking.com introduces smart filters that use AI to interpret user intent and automatically activate the most relevant filters.

Illustration labeled “Strategy 3: Structure” with a web layout icon and the text “Provide a compositional logic that organizes dialogue and outcome.”

Based on the user’s goal, surface key parameters and dimensions at the outset to turn unstructured requests into a flexible structural framework.

When generating with AI, every form of content carries a set of embedded decisions that directly shape the outcome.

For example:

  • 🖼️ Image generation: deciding on the aspect ratio, composition, and visual style.
  • 💬 Translation: defining the source and target languages, and sometimes the tone of equivalence.
  • ✍️ Writing: specifying the length, tone, and intended purpose of the text.

Structure helps identify these decisions in advance and exposes them during input. This form of pre-structured guidance reduces unnecessary iteration and produces results that better match user expectations.

Feature

  • Structure Generator: Once the user selects a task, the system identifies its key contextual factors and presents a configurable prompt structure, using selectable fields or options to guide input.

Example

Screenshot of the Bench interface showing a modular prompt builder where users combine task types, connected apps like Slack or Notion, and output formats such as documents, memes, or PowerPoint files.

Luke Wroblewski demonstrated an interesting “Lego-style” prompting example in Bench, where choosing the task type, tool channel, and output format automatically builds a structured prompt.

Screenshot of Monica AI’s interface organized into four sections — Image Generation, Writing, Translation, and Form Generation — each providing structured input fields and style options for different content creation tasks.

When you choose different content types in Monica AI, the system displays distinct prompt-extension interfaces, each offering relevant preset parameters.

Similar approaches have been widely explored in Chinese AI products at the application layer.

Prototype

Using image generation as a use case, I designed an advanced image generator that extends the chat interface into a configurator.

Animated demo of the “Advanced Image” generator. The user clicks to expand the panel, selects options such as aspect ratio, image type, and style, then clicks “Generate” to produce an image preview based on the chosen settings.

It lets users fine-tune parameters while composing prompts, producing results that are more precise and aligned with intent.

Screenshot of an “Advanced Image” generator interface showing options for aspect ratio, style, and image type, alongside a generated photo of hands holding a hummingbird flying through a flower-filled forest.

For AI products, prompt engineering is as essential as sign-up or onboarding, because it marks the start of collaboration.

Unlike traditional interfaces where intent emerges along the way, AI requires users to express it clearly and structurally from the very beginning.

The design task then lies in bridging the transition from user intention to clear articulation. And this is the very purpose of prompt-building interfaces.

Graphic titled “Prompt-Building Interface Design Strategy” showing three strategies: Guide (with a lightbulb icon), Constrain (with a vault icon), and Structure (with a web layout icon).

It means giving form to intention, setting boundaries for meaning, and composing structures that allow intelligence to respond.

In doing so, we are not just building interfaces, but cultivating a shared language between human intent and machine interpretation.

📖 Further reading

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Design prompt-building interfaces was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

 

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