Chat-based AI UIs are failing most users! We need a new pattern.
Chat-based AI interfaces are everywhere, but they’re not working for most users. While they seem simple, they struggle with complex tasks, leaving users frustrated and stuck in endless trial-and-error loops. From losing context to providing irrelevant answers, chat UIs create more problems than they solve for anything beyond basic requests.
The solution? Structured interfaces like GUI-based prompt builders. These tools replace freeform text boxes with dropdowns, sliders, and templates, guiding users step-by-step. They simplify communication, reduce errors, and make advanced AI capabilities accessible to everyone - not just experts in crafting prompts.
Key Problems with Chat UIs:
- Poor for complex tasks: Sequential text-based interactions make it hard to manage workflows or refine ideas.
- Lack of guidance: Users don’t know what the AI can do or how to ask for it.
- Frustrating trial-and-error: Crafting the “perfect” prompt wastes time and leads to inconsistent results.
Why Prompt Builders Work Better:
- Visual tools: Dropdowns, sliders, and templates provide clarity and structure.
- Domain-specific options: Tailored inputs ensure AI understands industry-specific needs.
- Better collaboration: Teams can standardize prompts, reuse templates, and achieve consistent results.
Bottom line: Chat UIs are a temporary step in AI design. For better user experiences and outcomes, structured interfaces like prompt builders are the way forward.
Why Chat UIs Fail at Complex Tasks
Users Can't Express What They Want Clearly
One major reason chat-based AI struggles with complex tasks is that users often fail to express their needs clearly. In fact, only about 10% of users are skilled at prompt engineering, leaving the vast majority frustrated when trying to communicate their intent through chat interfaces.
For most people, specifying constraints or providing the necessary context is a daunting task. This creates a frustrating loop of trial and error, especially since chat UIs require users to describe what they want, rather than how to achieve it. When dealing with intricate tasks like designing workflows or analyzing data, users must convey multiple requirements and constraints in a single prompt - something that’s incredibly difficult to do effectively.
This challenge often leads to what’s known as "exhaustive prompt tweaking." Users end up experimenting with countless variations of phrasing, tweaking words, and adding details in the hope of finding the perfect combination that delivers the desired result. For example, a simple instruction like "make this faster" could mean improving processing speed, enhancing user experience, or reducing load times. The AI is left to interpret the request, often guessing incorrectly.
These communication barriers make it nearly impossible for chat UIs to handle complex tasks effectively.
Chat Doesn't Help Users Learn What's Possible
Another major issue is that chat UIs fail to reveal the full range of their capabilities. They provide no visual guidance, leaving users with just a blank text box and no clear idea of what the system can actually do. Unless users already know the right questions to ask, they may never discover advanced features or possibilities, which leads to underutilization.
Think about it: when you open a chat interface, there are no menus, buttons, or visual cues to guide you. Traditional software interfaces use visual elements to show available actions, but chat UIs lack this kind of support. As a result, users are forced to rely on trial and error to figure out what’s possible.
This lack of guidance creates a significant "discovery problem." Users might spend weeks using an AI tool for basic tasks, completely unaware that it could handle far more sophisticated work. The limitation isn’t the technology itself - it’s the user’s lack of awareness about what the tool can do.
Without visual cues or structured input fields, it’s also hard to know what kind of information the system needs. When setting up something complex, like a workflow or detailed content, users need clarity on available parameters, expected formats, and constraints. Chat interfaces, however, provide none of this information.
The impact of these shortcomings is clear. Recent data shows that only 8% of customers used a chatbot for support, and of those, just 25% said they would use it again. This low satisfaction rate stems from chatbots’ inability to resolve complex issues and the frustration caused by repeated, unhelpful responses.
Because of these challenges, users rarely experience the "wow moments" that advanced AI can deliver. Instead, they remain stuck in basic interactions, unable to push the boundaries of what the technology can actually do. This highlights the need for a structured prompt builder to bridge the gap and unlock the system’s full potential.
GUI-Based Prompt Builders: A Better Solution
What Prompt Builders Actually Are
GUI-based prompt builders are visual tools designed to simplify how users interact with AI. Instead of relying on open-ended text boxes, these interfaces use familiar elements like dropdown menus, checkboxes, and form fields to guide users through the process. They combine the user-friendly structure of traditional software with the advanced capabilities of AI systems.
For example, tools like Google’s Imagen include interfaces where users can select from a limited number of options - such as four choices per dropdown - to streamline the process. This approach removes much of the trial-and-error frustration often associated with freeform chat interfaces.
By organizing complex AI interactions into clear, manageable steps, these builders make the process intuitive. Users can adjust parameters with sliders, choose from predefined settings, and see exactly what information is needed for the AI to perform well. This structured layout eliminates ambiguity, making it easier to communicate effectively and get the desired results.
How Prompt Builders Fix Communication Problems
One of the standout benefits of GUI-based prompt builders is their ability to address the communication challenges many users face. Studies show that almost half of adults struggle with writing detailed or complex instructions. By replacing lengthy written prompts with simple, visual selections, these tools make AI more accessible to everyone.
Platforms like Freepik and Leonardo AI demonstrate this well. Freepik organizes visual styles into clear categories - like color, lighting, and camera effects - allowing users to select specific attributes without needing to describe them in detail. Similarly, Leonardo AI refines user inputs automatically and offers prompt suggestions, making it easier to express complex ideas. Instead of typing out something like "cinematic lighting with warm tones", users can simply pick a visual example that matches their vision.
Structured fields also ensure consistency. By breaking down the input into specific sections, these tools remind users of what’s required, reducing the chances of missing key details or providing unclear instructions.
"Prompt augmentation features enable users to refine or modify their inputs, allowing the AI to better capture their intent. Users are no longer limited to the prompt text (written or spoken) they can produce from their brains, but get a richer prompt with AI's help. This helps overcome the articulation barrier in using AI." - Jakob Nielsen
This combination of simplicity and precision makes even complex AI tasks more approachable, encouraging broader adoption and better results.
Why GUI-Based Prompt Builders Work Better
The success of GUI-based prompt builders lies in their ability to simplify complexity. For instance, Perplexity saw user engagement double after introducing follow-up questions as part of its interface - a feature that guides users through structured interactions. This highlights how providing clear, step-by-step alternatives can make AI tools more effective and user-friendly.
Visual interfaces are particularly well-suited for tasks involving complex outputs or intricate visual data, such as media creation, workflow design, or product development. They allow users to see multiple options at once, compare settings, and understand how different parameters interact. This visual clarity helps users make informed decisions without the mental strain of crafting detailed prompts from scratch.
Reducing cognitive load is another key advantage. Instead of juggling multiple ideas while writing a prompt, users can rely on visual layouts where all options are clearly displayed. This is especially useful for multimodal AI systems or tasks involving abstract concepts that are hard to describe with words. Tools like ChatGPT Canvas, which uses sliders for parameter adjustments, and Kling’s motion brush, which enables precise selections, further simplify the process.
Additionally, the structured nature of these tools fosters collaboration and repeatability. By saving and versioning prompts along with their outputs, users can build on past work and share successful strategies with others. This not only enhances productivity but also increases the likelihood of achieving impressive results quickly, making the AI’s capabilities more accessible and impactful.
Building Better AI Products with Prompt Builders
Building Your Expertise into the Interface
Prompt builders do more than reduce ambiguity; they let you weave your professional expertise directly into the interface. Unlike generic chat interfaces that treat every user the same, prompt builders allow businesses to create AI tools tailored to their specific processes, terminology, and expertise.
Take Bloomberg's BloombergGPT, for example - a 50-billion-parameter model trained on decades of financial data. It outperformed general models on financial tasks. Similarly, Google's Med-PaLM 2, trained on curated medical datasets, achieved an impressive 86.5% score on the MedQA dataset. As Gideon Mann, Head of Bloomberg's ML Product and Research team, emphasizes:
"Dataset quality directly impacts the model performance".
By embedding domain knowledge, you can create AI copilots tailored to your industry. For instance, a legal prompt builder might feature dropdowns for contract types, checkboxes for common clauses, and templates for jurisdiction-specific documents. A marketing tool, on the other hand, could include options for campaign organization and brand voice customization. This approach transforms generic AI into specialized tools that truly understand your business needs.
An essential part of this process is integrating the problem-solving strategies - known as heuristics - that professionals rely on daily. By incorporating expert case studies, guidelines, and workflow-specific templates, you eliminate the need for users to translate their expertise into AI-friendly terms. This makes the tools more intuitive and effective from the start.
Chat UIs vs. Prompt Builders: Side-by-Side Comparison
To see how embedding expertise changes the game, let’s compare chat interfaces with prompt builders. While chat UIs depend on natural language and probabilistic outputs, prompt builders provide structured, predictable interactions that align with user intent.
| Factor | Chat UIs | Prompt Builders |
|---|---|---|
| Learning Curve | Users must learn prompt engineering skills | Guides users with structured workflows |
| Consistency | Output varies based on phrasing | Predictable outputs with clear input-output links |
| Complex Tasks | Struggles with multi-step workflows | Handles complex, multi-parameter tasks with ease |
| Domain Knowledge | Offers generic responses | Incorporates industry-specific context |
| Collaboration | Hard to share or standardize prompts | Enables reusable templates and team collaboration |
| Error Handling | Users troubleshoot unclear responses manually | Built-in validation and guided corrections |
The main issue with chat interfaces is their reliance on natural language, which often creates an illusion of control while hiding the system's actual limitations. Users are forced into a frustrating "prompt discovery" process, experimenting with different wordings to achieve the desired result. This trial-and-error approach doesn’t scale well for complex business workflows or specialized industries. For instance, a financial analyst shouldn’t have to figure out how to describe a risk assessment model in plain language when they could simply select a pre-built option from a structured interface.
Prompt builders solve these problems by introducing clear, shared abstractions. They bridge the gap between user intent and system capabilities by creating what experts call "a shared language of design". This clarity becomes even more critical for team collaboration. With chat UIs, each team member might develop their own prompting style, leading to inconsistent results across the organization. Prompt builders, on the other hand, support modular designs that allow teams to work in sync, share resources, and improve systems incrementally.
For companies scaling AI products, this structured approach is a game-changer. Instead of requiring every user to become a prompt engineer, you can embed expertise directly into the interface. This ensures consistent, high-quality outputs, regardless of individual skill levels.
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Design patterns for AI interfaces with Vitaly Friedman
How to Add Prompt Builders to Your AI Product
If you're looking to address the challenges of chat-based UIs and harness the potential of GUI-based prompt builders, here's a guide to integrating them into your AI product.
Finding the Right Use Cases for Prompt Builders
The first step is figuring out where your current chat interface might be causing friction for users. Take a close look at your features to pinpoint areas where users struggle with open-ended prompts.
Identify tasks that need specific formats or detailed instructions. These are the scenarios where structured approaches outperform free-form conversations. For example, in April 2025, Productboard outlined six key areas in product management where AI, guided by effective prompts, can simplify workflows: PRD generation, user research synthesis, competitive analysis, roadmap prioritization, stakeholder communication, and user persona development.
Look for workflows that require sequential input. Tools for creating marketing campaigns, for instance, often demand multiple structured inputs, making them ideal candidates for guided interfaces.
Dive into your support tickets and user feedback. Pay attention to repeated questions like, "How do I make the AI understand my industry?" or "Why isn't it generating what I need?" These reveal pain points where a well-designed prompt builder could make a difference. Focus on areas where your expertise can be integrated directly into the interface to guide users more effectively.
Finally, consider use cases where consistency is more important than creativity. Tasks like legal document drafting, financial analysis, technical documentation, and compliance reporting demand precision and reliability, making them better suited for structured inputs rather than open chat interfaces.
Creating Reusable Prompt Templates
To build effective prompt templates, you need a deep understanding of both your users' workflows and the capabilities of your AI model. Start by documenting successful user prompts to uncover common patterns.
Design templates with clear input-output relationships. Each template should include specific fields for user input, pre-written context that incorporates your domain expertise, and clear formatting instructions for the AI. For instance, a content strategy template might include fields for target audience, content type, key messages, and tone, while automatically embedding brand guidelines and SEO best practices.
Keep templates modular and flexible. Allow users to mix and match different components based on their needs. For example, a marketing prompt builder could include separate modules for audience targeting, campaign goals, creative direction, and performance metrics. This modular setup gives users the freedom to customize while maintaining the structure needed for high-quality outputs.
Before rolling out templates, test them with real users. What seems intuitive to your team might not align with how users approach their tasks. Iterate based on user behavior. If certain fields are consistently skipped or outputs are frequently adjusted, refine the templates to better meet user needs.
Document your templates thoroughly so team members can easily understand, modify, and expand them. Include examples of successful outputs, explanations of why certain elements work, and guidance on when to use each template. This documentation becomes critical as your library of prompts grows and new team members join the project. Once finalized, encourage collaboration within your team to continually improve and optimize these templates.
Getting Your Team to Work Together
Integrating prompt builders into your AI product requires seamless collaboration between UX designers, AI developers, and product managers. Each team brings unique insights that are essential for creating a user-friendly and effective solution.
Start with cross-functional planning sessions to align on user needs, technical constraints, and business goals. UX designers can provide insights into user workflows and interface design, developers can outline what's technically feasible, and product managers can prioritize features based on their impact on users and the business.
Make sure your prompt builder works well with existing tools like Figma, React, and Jira.
Train your team to understand how prompt engineering influences user experience and technical development. This shared knowledge ensures everyone is on the same page when it comes to designing and implementing prompt builders.
Keep track of how AI tools are supporting collaboration across the team. Regular check-ins can help identify bottlenecks, communication gaps, and areas for improvement. Set up feedback loops so team members can share what's working, what isn't, and how processes can be fine-tuned.
Remember to balance AI and human input. AI should act as a support tool, not a replacement for human expertise. While prompt builders can streamline workflows and improve efficiency, decisions about interface design, feature implementation, and prioritization should remain in the hands of designers, developers, and product managers. AI can assist by summarizing content, grouping ideas, or even generating action plans, but human judgment is crucial for making strategic calls.
Conclusion: AI Interfaces Need Structure, Not Just Conversation
Chat-based AI interfaces often fall short when tasks become intricate or demand precision. While these conversational systems may feel intuitive, they tend to create unnecessary obstacles for users with specific objectives.
To address these challenges, the focus needs to shift toward structured interactions. Instead of relying on users to craft perfect prompts, structured interfaces - like GUI-based prompt builders - offer a more user-friendly alternative. These tools guide users by providing visual cues, predefined pathways, and workflows grounded in domain expertise. This approach not only simplifies the user experience but also improves task efficiency and output quality.
According to research from the Nielsen Norman Group, nearly 50% of users struggle to communicate their intentions in a way that AI can interpret effectively. Structured interfaces bridge this gap by transforming AI from a "guess-what-I-mean" black box into a collaborative partner. The result? Faster task completion, higher-quality results, and greater user satisfaction. By shifting to this model, AI becomes a tool that actively supports rather than frustrates its users.
For companies, adopting structured interfaces is more than just a design upgrade - it's a competitive necessity. Prompt builders are no longer optional; they are essential for AI products aiming to serve a broad audience, not just the niche group skilled in prompt engineering. The future of AI lies in systems that prioritize structure over pure conversation.
Main Points to Remember
- Chat interfaces struggle with complex tasks, placing too much responsibility on the user. With only 8% of customers using chatbots for support and just 25% willing to try them again, it's clear that open-ended conversational UIs create more issues than they solve.
- Prompt builders offer clarity and guidance, leveraging domain expertise and proven workflows. By using templates, visual aids, and clear options, they enable users to discover features and achieve better results than traditional chat interfaces.
- Structured interfaces empower users by providing the tools they need to succeed quickly and efficiently. Organizations that embrace this shift will position AI as a reliable and accessible tool for a wider audience, not just for those who excel at crafting prompts.
The path forward is clear: structure, not conversation, is key to building AI interfaces that truly meet user needs.
FAQs
Why don’t chat-based AI interfaces work well for complex tasks?
Chat-based AI tools often falter when it comes to handling more intricate tasks. They often lack the clarity and flexibility needed for users to express their needs clearly, which can lead to confusion and frustration. When dealing with complex scenarios like designing systems, managing workflows, or creating products, chat interfaces can feel restrictive and cumbersome.
One key issue is that most users aren’t naturally skilled at crafting precise prompts. This often leads to a tedious trial-and-error process, wasting valuable time. A better approach? Incorporating visual and guided interfaces, like prompt builders, which provide a more user-friendly and efficient way to interact with advanced AI tools. These interfaces help bridge the gap, making complex tasks much easier to tackle.
How do GUI-based prompt builders enhance AI user experiences?
GUI-based prompt builders enhance how users interact with AI by introducing visual tools that streamline complex tasks. Instead of staring at a blank slate and guessing where to begin, users can rely on intuitive options that guide them in exploring possibilities and clearly expressing their needs.
These interfaces cut down on confusion by focusing on specific use cases, making interactions with AI quicker, more straightforward, and much more enjoyable. By integrating domain expertise, they empower users to achieve meaningful results efficiently while keeping control and clarity at the forefront of their workflows.
How does integrating domain-specific knowledge improve AI prompt builders?
Integrating domain-specific knowledge into AI prompt builders helps create outputs that are precise and highly relevant to particular industries or contexts. By customizing the AI's responses to fit specific needs, this method reduces confusion and simplifies workflows, allowing users to achieve their goals more efficiently.
When expertise is built into the system, AI becomes better at identifying patterns, processing details, and delivering accurate results. This not only boosts user satisfaction but also cuts down on the guesswork, saving valuable time and effort. In short, domain-specific prompt builders make AI tools easier to use, more effective, and better suited for practical, real-world tasks.