Technology

Why AI Frontend Infrastructure Is the Most Overlooked Layer in Your LLM Stack

Parikshit Deshmukh

17 Jun 2025 • 38 min read

Meta Description: Discover why AI frontend infrastructure is the missing layer in LLM applications. Learn how Generative UI and dynamic, LLM-driven interfaces transform AI user experiences.

Introduction
Large language models (LLMs) have transformed what software can do, but how much users benefit from that power depends heavily on the user interface. In the typical LLM tech stack, teams obsess over model performance, data pipelines, and prompt engineering yet often deliver the AI through a simple chat box or static form. This frontend layer is frequently overlooked, despite being the bridge between AI capabilities and user experience. In fact, the breakthrough success of ChatGPT illustrated how crucial a good interface is: a straightforward chat UI “unlocked massive adoption of a sophisticated LLM” by turning a complex AI into an everyday tool (Bridging The Gap). Conversely, many cutting-edge AI projects struggle to gain users because their interfaces are rigid or unintuitive (Bridging The Gap). The front end matters more than ever in the age of LLMs, and ignoring it can leave even the smartest AI underutilized. In this post, we explore why AI frontend infrastructure the tools and frameworks for building AI-driven, dynamic user interfaces - might be the most overlooked layer of your LLM stack, and why it’s critical for delivering real value from generative AI. We’ll discuss the limitations of today’s AI user experiences, how Generative UI is emerging to address these gaps, what an AI frontend API like C1 by Thesys offers, and why AI-native software needs a new approach to front-end design. By the end, you’ll see how bringing AI and UI together can turn raw LLM power into intuitive, engaging applications.

Why the Frontend Matters More in the Age of LLMs
As AI capabilities leap forward, the interface through which users interact with AI becomes a determining factor in success. There is often a noticeable “AI-UI gap” in modern applications: organizations invest in advanced models and back-end systems, but the end-user still interacts via a static web or mobile UI that barely hints at the AI’s sophistication (Bridging The Gap). This gap isn’t just cosmetic - it leads to frustration and lost opportunities. Users may never tap an AI’s full potential if the interface is too rigid or confusing. In practice, many AI tools see poor adoption not because the models are weak, but because the user experience is lacking. One industry analysis noted that technical excellence can backfire if usability is neglected (Bridging The Gap). Simply put, even the smartest AI needs a smart interface. The rise of conversational AI has shown that people expect more natural, adaptive interactions. Gartner analysts observe that AI is ushering in a new paradigm of UIs moving from static screens to more conversational and context-driven experiences (Bridging The Gap). Yet most organizations are still figuring out how to build such interfaces. It’s telling that while two-thirds of companies are exploring AI agents to automate tasks, “building a usable frontend for AI agents remains a major hurdle” in practice (Bridging The Gap). Enterprises racing to deploy LLMs have learned that users won’t embrace AI tools without a compelling interface. No matter how powerful your model is, if the UI doesn’t meet users where they are, adoption will suffer. The frontend is thus increasingly the make-or-break layer for LLM-powered products.

Problems with Current AI UX and Static Interfaces
Today’s AI user experiences are often hampered by static, one-size-fits-all interfaces. The classic chatbox UI, while simple, treats every user and query the same. This static design leads to several problems:

Weak engagement: Users expect rich, visual interactions, but many LLM apps return plain text only. This can feel underwhelming and fails to capitalize on multi-modal output (images, charts, etc.) that AI could generate (Bridging The Gap). An interface that doesn’t adapt or show information in the format the user needs will likely lose their interest.
One-size-fits-all design: Hardcoded UIs can’t adjust to different users or evolving context. Every user gets the same layout and options, even if they have distinct goals. The UI doesn’t reflect the AI’s “understanding” of the query. This rigidity means the interface often doesn’t present the most relevant controls or info for the task at hand (Bridging The Gap). Users are left navigating unnecessary menus or manually extracting info, instead of the software intuitively adjusting to them.
Slow iteration: Traditional front-end development for AI apps is time-consuming. Teams might spend months crafting a dashboard or form for a specific AI use case, only to find that by launch time, user needs have changed or the AI has new capabilities (Bridging The Gap). Static UX is slow to update, causing a lag between what the AI can do and what the UI exposes. This misalignment can render new AI features invisible to users.
Inconsistent experiences: If developers try to bolt on new UI elements piecemeal, the result can be a disjointed experience. Many current AI products default to a bland chat interface or a generic dashboard, which often doesn’t do justice to the AI’s intelligence (Bridging The Gap). The lack of an AI-aware design leads to interfaces that feel “dumb” even when the underlying model is brilliant.

These issues are increasingly documented. As InfoWorld reported, development teams often pour significant effort into front-ends for AI “only to deliver static, inconsistent, and often-disengaging user experiences” (Bridging The Gap). Such outcomes not only disappoint users but also hurt business value. If the interface doesn’t let users easily interact with the AI’s capabilities, much of the AI’s potential goes unrealized (Bridging The Gap). In summary, current AI UX tends to be too static and generic. This status quo creates friction for users and stifles the promise of AI-driven software. The good news is that a new approach is emerging to overcome these limitations.

What AI Frontend Infrastructure Solves (and How It Works)
AI frontend infrastructure refers to the frameworks and tools that enable dynamic, AI-generated user interfaces essentially, the infrastructure for Generative UI. Its goal is to solve the UX problems above by making interfaces more adaptive, context-aware, and efficiently developed. How does it work under the hood? Let’s break down the key components:

LLM-driven interface logic: At the core is an AI model (often an LLM) that interprets user input and decides not just what to reply, but how to present that reply. Instead of producing only raw text, the model is prompted or fine-tuned to output UI component specifications as well (Bridging The Gap). For example, if a user asks a question, the AI might determine that a chart or a form is the best response and output a structured description of that UI (e.g. “Display a line chart of sales over time, X-axis months, Y-axis revenue”) (Bridging The Gap). This requires the model to have some format or schema for describing UI elements (often JSON or similar). In essence, the AI becomes a decision-maker for the presentation layer, not just the data or text.
Library of UI components (LLM UI components): The AI doesn’t create UI from scratch - it works with a toolbox of pre-built components. An AI frontend infrastructure includes a library of standard UI components (charts, tables, buttons, text fields, form layouts, etc.) that the AI can summon as needed (Bridging The Gap). These are sometimes called LLM UI components - widgets designed to be controlled by AI outputs. For example, Thesys’s open-source Crayon library and other projects like llm-ui provide components (a streaming chat panel, dynamic form builders, map widgets, etc.) optimized for AI control (Bridging The Gap). The component library ensures that whatever the AI “specs out” adheres to the app’s design system and can be rendered consistently. Think of it like giving the AI a set of LEGO blocks - it can decide which blocks to use and how to arrange them, but the blocks themselves ensure visual consistency and functionality.
Rendering and front-end runtime: Once the AI outputs a UI specification and the components are identified, a rendering engine on the frontend takes over. This is typically a frontend framework (e.g. a JavaScript/React layer in a web app) that can interpret the AI’s instructions and instantiate the actual UI components in real time (Bridging The Gap). In other words, there’s a bit of code that acts as the bridge between AI output and on-screen interface. It reads the structured response (say, a JSON saying “create a table with these entries and a chart with this data”) and calls the appropriate UI components to display it. Modern generative UI systems use this kind of frontend automation: developers integrate the AI model and component library into the app, then much of the UI assembly is handled by the AI’s runtime outputs (Bridging The Gap). Instead of hand-coding every screen or state transition, the front-end infrastructure lets the AI dynamically build out the interface. It’s like having a mini front-end developer running inside the app, automatically laying out elements based on the AI’s decisions.

By combining these pieces - an LLM that outputs UI specs, a palette of UI components, and a runtime to render them - AI frontend infrastructure enables what we call Generative UI in practice. Developers gain a powerful new workflow: rather than enumerating every possible dialog or page in advance, they define high-level guidance (prompts, design constraints, component library) and let the AI generate the interface on the fly. This approach addresses the earlier UX problems by making the UI adaptive (since the AI can change it per context) and speeding up development (since a lot of UI is assembled automatically). As one expert noted, developers can stop hardcoding every workflow and “let an AI model generate live UI components based on prompt outputs,” drastically reducing the manual scaffolding of front-end development (Bridging The Gap). In short, AI frontend infrastructure automates the heavy lifting in UI creation. This not only yields more dynamic interfaces for users, but also accelerates the build process for developers.

What Is Generative UI? (And LLM UI Components Explained)
At the heart of AI frontend innovation is the concept of Generative UI. Generative UI (GenUI for short) refers to user interfaces that are dynamically generated by AI in real time, rather than designed and coded entirely by humans beforehand (Bridging The Gap). It’s a fundamentally new approach to UI/UX. In a generative UI, the application’s layout, components, and content can morph on the fly, tailored to each user’s needs, intent, and context. Nielsen Norman Group, a leading UX research firm, defines a generative UI as “a user interface that is dynamically generated in real time by artificial intelligence to provide an experience customized to fit the user’s needs and context.” (Bridging The Gap) In practical terms, generative UI means you’re not giving every user the same static screens. Instead, the interface assembles itself based on what the user is trying to do, their preferences, and even their past behavior. It’s like having a digital UX designer in the loop every time someone uses your app, continuously tweaking the layout to best serve that individual (Bridging The Gap).

Consider a simple example: a data analytics dashboard. Traditionally, product designers would create a one-size-fits-all dashboard with a fixed set of charts and filters. Whether or not certain charts are relevant to a particular user, they’re all there, and it’s on the user to ignore or hide what they don’t need. In a Generative UI approach, an AI-powered dashboard could reconfigure itself intelligently: if the system knows, say, that a user cares mainly about sales by region, it might automatically highlight a sales-by-region chart and hide other charts behind tabs or accordions (Bridging The Gap). For another user focused on, say, marketing metrics, the interface might surface those instead. The interface literally “designs itself” for each session, so the user isn’t stuck with an overly cluttered or irrelevant screen - instead, the software adjusts to them (Bridging The Gap). This real-time personalization goes far beyond basic theme settings; it’s an adaptive UI driven by AI understanding of what each user needs.

It’s important to clarify that Generative UI is different from the recent spate of AI design tools that convert prompts into code or mockups. You might have seen “prompt-to-UI” tools that generate a static design or front-end code from a description (e.g. “generate a sign-up form with three fields and a submit button”). Those can speed up development, but they operate before an app is live - helping a human designer or developer create a UI that then remains mostly fixed. Generative UI, by contrast, refers to the UI during runtime: it’s the interface continuously being created and updated by AI as the user interacts (Bridging The Gap). One Thesys article put it well: prompt-to-UI tools instantly turn ideas into code, whereas Generative UI “revolutionizes how people experience software by letting the interface shape itself in real time, uniquely for them.” (Generative UI vs Prompt-to-UI vs Prompt-to-Design). In summary, prompt-to-UI is about using AI to help build the interface ahead of time, while Generative UI is the interface being built (and rebuilt) by AI, in the moment. Generative UI often leverages the same AI models that drive the app’s core logic to also drive the presentation. In other words, the LLM that answers your question might also decide to render that answer as a table or a graph, effectively making the UI an extension of the model’s reasoning (Bridging The Gap). This is sometimes called an LLM-driven product interface, meaning the UI is part of the output of the LLM. For instance, if you ask an AI agent, “Compare Product A vs Product B sales this quarter,” a generative UI system might return not just text but an interactive chart comparing A and B, perhaps with filter dropdowns for the time range (Bridging The Gap). The LLM’s output isn’t just words, but UI instructions to show a comparison in the most understandable way. The result is a much more engaging experience than a static block of text or a pre-built dashboard from last quarter. In essence, Generative UI lets the AI “speak” in UI elements (charts, forms, buttons) and not only in natural language (Bridging The Gap).

To enable this, developers rely on LLM UI components, as mentioned earlier. These are the building blocks that an LLM can call upon to construct the interface. They might include things like a Chart component, a DataTable component, an InputForm component, etc., which accept parameters (data, labels, options) that the AI fills in. The AI’s job is to decide when to use which component and with what configuration. The component library’s job is to actually implement the visuals and ensure consistency. By providing standardized components, we make it feasible (and safe) for an AI to generate UI without breaking design norms. Platforms like the Crayon SDK or libraries like llm-ui already offer such components to developers (Bridging The Gap). For example, an LLM output might include a JSON like: { "component": "Chart", "title": "Sales Q4", "data": [ ... ] }. The frontend runtime will map that to the Chart component from the library and render it. This separation of concerns is key: the AI focuses on what to show, the component library and frontend handle how to show it.

Generative UI is a powerful concept, but it also introduces new design considerations. If the interface can change for every user in real time, how do we ensure it remains understandable and user-friendly? UX experts caution that with great personalization comes the risk of confusion - users still need consistent visual language and navigation cues (Bridging The Gap). Ensuring an AI-generated UI is trustworthy and on-brand requires thoughtful guardrails. In practice, teams incorporate constraints and style guides that the AI must follow (for example, limiting color schemes, or always including certain safety info on screen). Despite these challenges, the trend is clear: software UIs are becoming more contextual, dynamic, and outcome-oriented (Bridging The Gap). Instead of forcing every user through the same clicks and menus, the interface can present exactly the right tools for that user’s goal. For designers and developers, this shifts the role from crafting one static design to defining the rules and components for an ever-evolving design. It’s a new paradigm - one that AI frontend infrastructure is built to support.

The Developer Experience: AI Frontend APIs and Frontend Automation
How does Generative UI affect developers and product teams? In a word, dramatically. Building interfaces with AI frontend APIs introduces a new developer experience (DevEx) that emphasizes high-level intent over low-level implementation. Rather than painstakingly coding every button state and layout by hand, developers can build UI with AI as a co-creator. This is often done through an AI frontend API - for example, making a request to an API that returns UI components based on an input. C1 by Thesys is one such API that exemplifies this new workflow. With C1 by Thesys, a developer can send a prompt or structured request describing what the user needs, and the API responds with a structured description of UI elements to render (Bridging The Gap). The developer then doesn’t have to manually assemble those elements; C1 by Thesys SDK or the developer’s front-end code will handle turning that into live UI. Essentially, you describe the desired outcome, and the AI frontend infrastructure figures out the UI implementation. This flips the traditional frontend development process on its head.

The benefits to developer productivity and velocity are significant. Instead of weeks of front-end coding and coordination with designers, teams can stand up functional interfaces in a fraction of the time. Routine interface logic (like creating forms or tables from data) can be automated. One report noted that by 2026, organizations using AI-assisted design and development tools will reduce their UI development costs by 30% and increase design output by 50% (Bridging The Gap). This kind of acceleration means smaller teams can deliver more features, and startups can iterate on product ideas faster. In the context of LLM apps, where you might be experimenting with new user prompts or workflows frequently, having an AI assemble the UI dynamically allows for rapid iteration without constantly rewriting front-end code.

From the developer’s perspective, working with an AI frontend API feels a bit like moving up a level of abstraction. You focus on defining prompts, examples, or rules for the AI, and on curating the component library. You might, for instance, spend time crafting a good system prompt that guides the LLM on how to format UI outputs (“If user data is numerical, consider a chart; if comparative, consider a table,” etc.). You also ensure the design system is encoded in the components and perhaps provide fallback behaviors or validations (for example, if the AI suggests an out-of-bounds UI element, your code can catch it). But you don’t have to write every UI screen yourself. As a result, developers shift from being “pixel pushers” to more of an architect or orchestrator role. An analogy someone made is that developers become more like architects than bricklayers when AI handles the repetitive UI construction. Freed from boilerplate tasks, developers can concentrate on higher-level concerns - overall user flow, integration of the AI with back-end data, performance, security, etc. Meanwhile, the AI takes care of generating the routine interface pieces and responding to user interactions in real time.

This collaboration between developer and AI can also improve the design-development workflow. Because so much UI can be generated on the fly, teams can prototype ideas extremely quickly. Product managers or designers can literally prompt new interface variants (“what if we showed this data on a map instead of a chart?”) and see a live version almost instantly, rather than waiting for a frontend engineer to code it. It enables more experimentation with UX. Also, because the AI uses components, it inherently keeps to a consistent style (assuming your component library is consistent), reducing the likelihood of human error causing off-brand or inconsistent UI. Developers might also use the AI to handle responsiveness (different device sizes) by having it adjust layouts as part of the generative logic, further saving time.

That said, adopting frontend automation does require new skills and mindsets. Prompt engineering becomes part of front-end development - you need to tell the model how to decide on UI elements. There’s also a need to implement guardrails and testing: verifying that the AI’s UI outputs make sense and are usable. In practice, developers will write unit tests or use rules to ensure, say, the AI’s JSON output conforms to a schema and doesn’t produce something nonsensical. It’s a bit like how front-end devs currently guard against invalid user input; here we also guard against invalid AI output. Fortunately, early adopters report that with a good initial setup, the AI UI generation is reliable and massively speeds up their work (Bridging The Gap). Teams using C1 by Thesys, for example, have noted significant speed-ups in delivering new features and dashboards, with less reliance on manual UI updates (Bridging The Gap). Essentially, once the AI frontend infrastructure is in place, adding a new feature might be as simple as extending the prompt or adding a new component to the library, rather than redesigning a whole page.

Another positive effect is improved cross-functional collaboration. When interfaces can be adjusted via prompts or configuration, designers and product folks can participate more directly in tweaking the UI behavior. It blurs the line between design and implementation in a productive way - a designer could prototype an interactive flow by writing out the desired behavior in natural language and having the system manifest it, rather than needing a coder for every change. Everyone speaks the “language” of high-level intent, which the AI translates into UI. This can shorten feedback loops and help ensure the final product matches the user experience vision.

In summary, AI frontend APIs and automation tools change the developer experience by automating repetitive UI coding and enabling dynamic interfaces. Developers focus on guiding the AI (through prompts and component libraries) and handling the complex logic, while the AI takes care of rendering the interface in real time. This leads to faster development, more flexibility, and potentially a more enjoyable process of building AI-native software. As one TechStartups article put it, AI-driven frontend automation is a less-talked-about but powerful revolution - it’s not just about coding faster, it’s about reengineering the entire lifecycle of UI development for AI. By adopting generative UI infrastructure, teams can innovate on UX at the speed of AI, not limited by front-end implementation bottlenecks.

Enterprise Examples: AI Dashboard Builders, AI UX Tools, and Real-Time Adaptive UI
What does AI frontend infrastructure look like in real-world applications? Let’s explore a few enterprise scenarios to make it concrete:

AI-Powered Analytics Dashboards: Business intelligence and analytics tools are embracing generative UIs to let users “ask” for insights and get dynamic visual answers. Imagine an analytics platform where a user can simply query, “Show me the top 5 products by growth this month,” and the application responds by generating a live bar chart highlighting those products. Using a generative UI API, the system can interpret that request, fetch the data, and assemble an interface element (a chart with appropriate labels and perhaps a dropdown to adjust the timeframe) on the fly (Bridging The Gap). The frontend isn’t limited to pre-built reports - it can create new visualizations as needed. This essentially turns the dashboard into an LLM agent user interface, where the LLM agent generates analysis and the UI to display it. Companies are already building AI dashboard builders in this vein, allowing non-technical users to interact with data through natural language and get interactive charts or tables instantly, without a developer manually creating that view in advance. This level of adaptability was almost impossible with static UIs; generative UI makes it feasible to have a truly real-time adaptive UI for data analytics.
E-commerce and Personalized Shopping UIs: In e-commerce, generative UIs enable on-the-fly personalization that can significantly enhance the shopping experience. Consider a virtual shopping assistant on a retail site - when a customer asks, “I’m looking for a running shoe under $100 with strong arch support,” a traditional chatbot would return a text list of recommendations. An AI-frontend-equipped assistant, however, could generate a mini dynamic catalog UI: it might display product cards for a few shoes, a comparison table of features, and even a follow-up prompt-generated form asking the user to refine preferences (size, color). All of these UI elements (cards, table, form) could be created in response to the query, using the component library. Generative UI in e-commerce can automatically create personalized shopping experiences, product recommendation panels, and checkout workflows on demand (Docs). For instance, if a customer frequently buys a certain brand, the AI could reconfigure the homepage to prominently show a widget for that brand’s new arrivals - without a human designer pre-programming that rule. Companies are beginning to use such AI UX tools to deliver tailored, context-aware interfaces that boost engagement and conversion. It’s like having a salesperson who instantly rearranges the store for each customer’s needs.
AI Copilots in Enterprise Software: Many enterprises are integrating AI copilots into internal software - for example, an AI assistant in your CRM or project management tool. AI frontend infrastructure helps here by blending the copilot’s intelligence with the existing UI seamlessly. Suppose you have a project management AI that can update tasks or generate status summaries. With generative UI, that AI could present its output as a dashboard widget or an interactive checklist inside the app, not just as a chat message. If you ask the copilot, “Summarize this week’s progress and flag any delayed tasks,” it could generate a formatted summary along with a colored list of delayed items and a button to reschedule them. That UI is generated on the fly, placed into your project management interface as if a developer had built a custom feature - except it happened in seconds via the AI. Enterprises are finding that such dynamic UIs for AI agents make the tools far more intuitive. Instead of the AI feeling tacked on, it becomes an integrated, visual part of the workflow. Real-world examples include AI copilots for sales teams that generate custom dashboards per client query, or AI assistants in IT dashboards that pop up relevant charts and forms for troubleshooting based on the conversation. These are essentially frontend for AI agents - interfaces that allow the AI to show its work and let users steer it with rich controls, rather than just text.
Education and Training Platforms: Adaptive learning software can use generative UI to tailor content delivery to each learner. For example, an edtech platform with an LLM tutor could generate interactive quizzes, highlight charts of progress, or show multimedia explanations in response to student questions - assembling these interface elements on demand. If a student is struggling with a concept, the AI could present an extra practice panel or a different visualization (say, a diagram) to aid understanding. Traditionally, designing a full set of tutorial screens for every student profile is impractical, but with generative UI the AI can mix and match components (video player, quiz form, flashcards, etc.) suited to the moment. Early trials indicate that such personalized, AI-driven interfaces can improve engagement and outcomes by responding immediately to learner needs (Bridging The Gap).

These examples scratch the surface, but they highlight a common theme: dynamic interfaces that adapt to the user’s request or context in real time. This is precisely what AI frontend infrastructure is built for. We now have the technology to build apps that no longer have a single static UI, but rather an LLM-driven product interface that can be different for each user and each moment. The result is higher user satisfaction and efficiency - users feel the software meets them where they are, showing exactly what they need when they need it. Businesses leveraging this are already seeing benefits. Thesys noted that over 300 teams are using its generative UI tools to deploy such adaptive AI interfaces, indicating rapid uptake in industries from finance to healthcare. These pioneers report higher engagement with their AI features and faster iteration on product ideas, as the interface is no longer a bottleneck (Bridging The Gap). The takeaway for enterprises is clear: whether it’s an AI dashboard builder for analytics, a generative UX tool for a shopping site, or a real-time adaptive UI in an internal system, investing in AI frontend infrastructure can be a game-changer. It unlocks use cases that were previously too costly or complex to build UI-wise, and it ensures your AI’s intelligence truly shines through to the end-user.

C1 by Thesys: The AI Frontend API Powering Dynamic LLM Interfaces
One notable example of AI frontend infrastructure in action is C1 by Thesys. C1 by Thesys is described as a Generative UI API (essentially an AI frontend API) that lets developers turn LLM outputs into live, interactive interfaces effortlessly. It implements all the pieces we discussed: an LLM-based engine that interprets prompts and yields UI specs, a robust library of UI components (Thesys’s “Crayon” React components), and a rendering mechanism to display the generated interface. To illustrate how C1 by Thesys works, consider a developer building an AI-driven analytics tool. Using C1, the developer sends a query and context to the C1 by Thesys API - for instance, a system prompt might say: “The user will ask questions about sales data. Respond with a JSON specification of UI components if applicable, not just text.” When an end-user asks “What were our top 3 regions by sales in Q2?”, C1 by Thesys backend LLM interprets this and might return a JSON response describing a bar chart component with the sales data and maybe a table of the top 3 regions. The C1 by Thesys React SDK on the frontend then takes that JSON and renders an actual bar chart and table in the web app UI, complete with any interactive elements (perhaps a filter to switch quarter). All of this happens in real time, triggered by the user’s query.

The result: the user sees a rich, dynamic answer (a chart + table) instead of just a paragraph of text. From the developer’s perspective, they did not have to code the chart or table explicitly for that specific query - C1 by Thesys handled it. They just integrated the C1 API and defined generally how the system should respond. As InfoWorld summarized, “C1 lets developers turn LLM outputs into dynamic, intelligent interfaces in real time”. It effectively generates the UI on the fly, so you can deliver new functionality without pushing new front-end code for every change.

Importantly, C1 by Thesys is model-agnostic in the sense that it works with any LLM that can follow the prompting format. It guides the LLM with a special system prompt so that the output comes in the expected structure (including the UI instructions). Developers can also include external tool functions - for example, if the UI needs data from an external API or database, C1 by Thesys can incorporate function calling to fetch that, then present it. This means the UI generation can be tied into real business data and logic securely. Thesys designed C1 by Thesys to support a wide variety of UI component types via its framework, from forms and charts to image galleries and dialogues. So, whether you’re building a customer service chatbot that needs to show troubleshooting steps, or a document analysis AI that should display highlighted PDFs, C1 by Thesys can likely handle the UI needs.

One of the powerful concepts with C1 by Thesys is using system prompts to guide UI generation. Developers can essentially “prompt engineer” the UI behavior. For instance, you might instruct the model: “If the user’s query is about comparing metrics, respond with a chart. If it’s a definition, respond with a text card.” These guidelines ensure the AI chooses an appropriate UI mode. This kind of high-level control is much faster than coding each scenario in React manually. And if the guidelines need to change (say users prefer tables to charts), you adjust the prompt or settings, not the entire codebase.

Security and control are also considered: C1 by Thesys runs within the application’s environment, and developers can put guardrails to review or post-process the AI’s UI outputs. It doesn’t mean surrendering all control to the AI - think of it as a supercharged UI engine that you supervise. You can always override or refine what it does.

The developer experience with C1 by Thesys has been likened to working with a very smart UI assistant. You initialize it in your app with a few lines of code, include the C1 by Thesys SDK, and define when to call it (e.g., on certain user actions or queries). The learning curve is relatively small - if you know how to call an API and render JSON, you can use C1 by Thesys. Thesys provides documentation and examples showing how to format prompts and handle the responses. Many developers report that after integrating C1 by Thesys, their front-end codebase shrinks, and their feature delivery speeds up, because they offload a lot of conditional UI logic to the C1 by Thesys API.

From a business standpoint, C1 by Thesys and similar AI frontend services address the “last mile” of AI integration. Companies that have invested in powerful LLMs and backends can plug in C1 by Thesys to immediately improve the UX without reinventing the wheel. Rather than building a custom generative UI framework in-house (which is complex and requires deep expertise in both AI and frontend), they can use a solution like C1 by Thesys that’s purpose-built for this. It’s analogous to how businesses use cloud APIs for vision or speech instead of training their own models - here, they use an AI UI API instead of building a full generative UI system from scratch.

The impact C1 by Thesys is aiming for is to make AI-native product interfaces the new norm. Already, more than 300 teams have been using Thesys’s generative UI tools (C1 by Thesys and others) to deploy adaptive interfaces. These range from startups to large enterprises. The fact that hundreds of teams have jumped on this in the early stages suggests a strong recognition that this layer of the stack needs attention. Thesys’s positioning of C1 is that of “frontend infrastructure for AI” - analogous to how we have back-end infrastructure (cloud, databases) for scaling servers, C1 by Thesys is infrastructure to scale and automate the UI for AI-driven apps. By integrating something like C1 by Thesys, companies essentially equip their stack with an “AI UX brain” on top of the LLM.

In summary, C1 by Thesys serves as a concrete example of AI frontend infrastructure in practice. It provides a GenUI API that developers can call to generate UI components from LLM outputs, complete with an SDK to render those components. It addresses the overlooked front-end layer by making it intelligent and dynamic. Whether or not one uses C1 by Thesys specifically, it illustrates the general approach: use AI to automate your front-end, so your product’s interface can keep up with the power and speed of your LLM back-end.

Why AI-Native Software Needs Its Own Frontend Infrastructure
AI-native software - applications built fundamentally around AI capabilities - represents a new breed of products. These are tools like AI copilots, autonomous agents, dynamic analytics apps, and adaptive learning platforms. Their core logic is driven by models that can reason and generate content. To fully realize the potential of such software, we need to rethink the front-end layer. Traditional UI/UX methodologies assumed relatively static requirements and human-designed interactions. But AI-native apps are living, evolving systems; their UIs need to be equally adaptive and intelligent. In short, AI-native software needs its own frontend infrastructure because the old front-end paradigms simply don’t suffice.

Firstly, consider the pace of change. AI capabilities can improve or change with a new model update or a new prompt. If every time the AI gets smarter, the UI needs a manual redesign, the product will lag behind its potential. An AI-aware frontend infrastructure allows the interface to evolve organically with the AI. For example, if your LLM gains the ability to output a new type of content (say it learns to create interactive graphs), a generative UI could start presenting that immediately, whereas a static UI would hide that talent until developers catch up. AI-native apps demand a frontend that can keep up with the AI’s evolution - something only possible when the frontend itself is partly driven by AI.

Secondly, AI-native software often involves complexity and ambiguity in user interaction. Users might ask open-ended questions, or the path to accomplishing a goal might not be linear. A static interface can’t anticipate all these paths gracefully, but an AI-driven interface can adjust on the fly. In effect, the UI becomes part of the AI’s problem-solving approach. If the user’s request is unclear, the AI can introduce UI elements to clarify (like follow-up question buttons or a disambiguation dialog). If the user’s task is complex, the AI can break it down and generate a step-by-step UI (like a wizard form) to guide them. These kinds of interactions blur the line between interface and logic - a good reason to have specialized infrastructure managing it. Traditional front-end code would struggle here; you’d end up writing countless conditional flows. AI frontend infrastructure handles this fluidity by letting the AI dynamically decide flows and layouts.

Another reason AI-native software needs specialized frontend tools is personalization at scale. AI enables software to be tailored deeply to each user (think: different content, different sequence of interactions, different feature set emphasis per user). Delivering this through manual UI development is incredibly expensive and complex. But an AI that knows the user’s preferences could literally rearrange the interface for them. For example, if an AI system knows a particular user is a novice, it could generate more tooltips and guidance UIs; for an expert user, it might hide the hints and show advanced options upfront. AI-driven frontend can do this instantaneously and contextually. To support it, we need frameworks that don’t hardcode one UI, but allow many possible UIs to be composed as needed. That is exactly the aim of GenUI approaches. As one article noted, generative UI shifts us from “designing for many to designing for one, at scale” - every user getting a custom-fitted interface rather than an off-the-rack design (Bridging The Gap). AI-native apps benefit hugely from this, because their value often lies in how well they adapt to each user (think of personal assistants, for example). Achieving this adaptability is only practical with AI in the loop of the front-end.

Finally, there’s a strategic angle: As AI becomes a key differentiator in software, the UX powered by AI will distinguish winners from losers. Companies have poured resources into AI models and infrastructure (the back end of the stack), but if the front end remains a plain chat window or a clunky form, users might not perceive the innovation. They’ll compare a static AI tool vs. a dynamic one and prefer the latter because it “feels smarter” and more helpful. AI-native products need AI-native interfaces to fully deliver a wow effect and tangible usability improvements. We’re essentially in a paradigm shift - some call it the first new UI paradigm in decades (Bridging The Gap) - where AI can proactively drive the interface. Those who adopt this paradigm will set new standards for user experience, and those who don’t may find their sophisticated AI features underused because the UI didn’t do them justice.

In summary, AI-native software changes the game on the front end. It introduces needs for continuous adaptation, context-awareness, personalization, and complex interaction handling that the existing front-end stack wasn’t built for. AI frontend infrastructure is the answer to that. It is the missing layer that ensures the intelligence of the system is matched by the intelligence of the interface. By investing in generative UI frameworks and AI-driven frontends, teams ensure that their AI’s brains are paired with the necessary “face” and “personality” to engage users. In the end, bridging the AI-UI gap is about making advanced technology more human-friendly (Bridging The Gap). When an AI system is intelligent on the inside and also smart on the outside, users can truly tap into its power (Bridging The Gap). AI frontend infrastructure is what makes software smart on the outside. It’s time this layer gets as much attention as the rest of the LLM stack, because it’s key to unlocking the full promise of AI in real applications.

Thesys and the C1 by Thesys API - Take the Next Step
If you’re ready to build AI-powered applications with interfaces that adapt in real time, consider exploring [Thesys] - the company pioneering Generative UI infrastructure. Thesys’s [C1 by Thesys API] is a Generative UI API (a kind of AI frontend API) that allows your LLMs to generate live, interactive UIs from their outputs. With C1 by Thesys, you can empower your AI tools to go beyond text and create charts, forms, buttons, and more on the fly. Thesys provides the building blocks and guidance to integrate this into your product quickly. Whether you’re building an AI dashboard builder, an AI-driven analytics app, or the next AI copilot, C1 by Thesys can help you deliver a dynamic user interface that keeps up with your AI’s intelligence. In short, Thesys enables your LLM to become not just the brain of your application, but also its creative UI designer - so you can deliver engaging, intuitive experiences faster and with less code. Check out Thesys.dev for more details and the docs.thesys.dev for documentation and tutorials, and see how AI frontend infrastructure like C1 by Thesys can elevate your LLM stack.

By following these steps incrementally, you can bring Generative UI concepts into your project without a complete overhaul. Even adding one dynamic element (say, an AI-generated chart in a dashboard) can add a lot of value and give you a foothold to expand further. The key is to treat the AI as a component of your UI stack and iterate as you would with any new technology. With practice, you’ll get a feel for how the AI “thinks” about UI and how to best harness it. Generative UI is still a new field, so expect some trial-and-error, but also expect delight when you see your app’s interface intelligently responding to users in ways you didn’t hardcode!

References

Moran, Kate, and Sarah Gibbons. “Generative UI and Outcome-Oriented Design.” Nielsen Norman Group, 22 Mar. 2024.
Krill, Paul. “Thesys Introduces C1 to Launch the Era of Generative UI.” InfoWorld, 25 Apr. 2025.
Schneider, Jeremy, et al. “Navigating the Generative AI Disruption in Software.” McKinsey & Company, 5 June 2024.
Louise, Nickie. “Cutting Dev Time in Half: The Power of AI-Driven Frontend Automation.” TechStartups, 30 Apr. 2025.
Firestorm Consulting. "Rise of AI Agents" Firestorm Consulting
Thesys. Bridging the Gap Between AI and UI: The Case for Generative Frontends. (Thesys Blog, 2025).
Thesys. What Are Agentic UIs? A Beginner’s Guide to AI-Powered Interfaces. (Thesys Blog, 2025).
Firestorm Consulting. “The ‘Agentic UI’ Pattern, or: ‘Giving Users a Colleague, Not a Button.’” Vocal.Media, May 2025.
Brahmbhatt, Khyati. “Generative UI: The AI-Powered Future of User Interfaces.” Medium, 19 Mar. 2025.
Firestorm Consulting. "Stop Patching, Start Building: Tech’s Future Runs on LLMs" Firestorm Consulting

FAQ
Q: What is AI frontend infrastructure?
A: AI frontend infrastructure refers to the tools, frameworks, and APIs that enable dynamic, AI-driven user interfaces. It’s the “front-end layer” specifically designed for AI-native applications. Traditional front-end tech results in static interfaces, whereas AI frontend infrastructure (like Generative UI systems) allows the UI to be generated or adapted by AI models in real time. This includes things like generative UI APIs, LLM UI component libraries, and runtime engines that render AI-created UI elements. In essence, it’s the part of the stack that lets an LLM or AI agent directly influence what the user interface looks and behaves like - sometimes called the “AI frontend” or “Generative UI” layer of the application.

Q: What is Generative UI (GenUI)?
A: Generative UI is a new approach to building user interfaces where the UI can be created or changed on the fly by an AI. Instead of a fixed, pre-designed interface, a Generative UI assembles itself in response to the user’s needs and context, often using an LLM or other AI to decide which components to show. For example, in a GenUI system, if a user asks a question, the AI might decide to show the answer in a chart, a table, or a set of bullet points - generating those UI elements in real time. GenUI stands for Generative User Interface, emphasizing that the UI is generated dynamically (usually by a generative model like an LLM). This is different from traditional UI (static UI) and even from AI design tools that generate code; GenUI happens live, during application use. It enables more personalized, context-aware, and interactive experiences because the interface isn’t one-size-fits-all. Each user interaction could potentially spawn a unique interface outcome. GenUI is closely related to AI frontend infrastructure - the latter provides the capability, while GenUI is the concept/practice of using it for adaptive interfaces.

Q: How does Generative UI differ from a normal (static) UI?
A: A static UI is designed upfront by humans - every button, layout, and screen is pre-defined and will only change if developers update the app. In contrast, a Generative UI is AI-driven and dynamic. The differences include:

Adaptivity: Static UIs show the same elements regardless of who the user is or what they need at that moment. Generative UIs can adapt to each user’s input or context (for instance, showing different information to a first-time user vs. an expert, or changing the layout depending on the query).
Real-time Generation: In static UI, the interface only changes through predetermined interactions (click a menu, see a submenu). In GenUI, the interface might change in real time as the AI “decides” the best way to present results. For example, after a question, an AI might generate a new panel with the answer and some follow-up options, even if that panel wasn’t explicitly coded by the developers beforehand.
Complex Responses: Static UIs typically handle one mode of output (text, or a chart, etc. as fixed parts of the design). Generative UIs can produce multi-modal, complex responses - the AI could return text and a chart and a set of action buttons together, if that makes sense for the situation.
Development process: Building static UI is a manual process. Building generative UI is more about training or prompting the AI and providing it components; the AI takes on some of the development work (at least at runtime). This means maintenance is different: GenUI might require monitoring AI outputs and tweaking prompts rather than rewriting code for every little UI change.
In short, Generative UI is dynamic and AI-curated, whereas static UI is fixed and designer-curated. The generative approach can create more personalized and efficient user flows, but it also requires ensuring the AI’s changes are user-friendly.

Q: What are LLM UI components?
A: LLM UI components are pre-built user interface elements that are designed to be controlled by a Large Language Model (LLM) or similar AI. They are the “building blocks” of a generative UI system. Examples might include components like a Chart, a Table, a Form, a Text Card, an Image Gallery, etc. Each component has configurable properties (for instance, a Chart component might take data series and labels as inputs). In an AI-driven frontend, the LLM can output a specification that calls for a certain component with certain parameters. The front-end then renders that component. The reason we call them LLM UI components is that they often come with a schema or format that an LLM can easily plug values into. They are also usually optimized for being created or modified at runtime. For example, an “AnswerCard” component might show text results with a consistent style - the LLM just needs to supply the text and perhaps a title. By having a library of LLM-compatible components, developers make it feasible for the AI to safely generate UIs without breaking design. These components ensure consistency (the AI isn’t literally drawing pixels; it’s selecting from known elements). Some open-source projects and platforms (like Thesys’s Crayon or the llm-ui library) provide such components, making it easier to build generative UIs. In summary, LLM UI components are the modular UI elements that an AI can use to construct an interface on the fly, analogous to how a human developer uses UI components in frameworks like React - except here the “developer” at runtime is the AI.

Q: How can an LLM create a user interface from a prompt?
A: An LLM can create a user interface from a prompt through a technique often called structured output prompting (or in the context of GenUI, prompt-to-UI). Essentially, the LLM is guided (via its prompt and training) to output not just free-form text, but a description of UI elements in a format that the application can understand. Here’s how it works in steps:

Prompting the LLM: The system (developer) provides a prompt that includes instructions and possibly a format. For example, the system prompt might say: “You are an AI that generates UI. When the user asks something, if a visual or interactive output would help, respond with a JSON describing the UI components. Use the format: { component: <type>, props: { ... } }. If text answer is sufficient, just provide text.” This is just an illustrative prompt, but basically the LLM is primed to know it can output JSON for UI.
User query and LLM decision: The user’s prompt/question goes to the LLM. The LLM, following its instructions, decides what the response should contain. Suppose the user asked for a comparison of data; the LLM “realizes” a chart is appropriate. Thanks to the prompt instructions and any examples it saw during training (or few-shot examples), it then constructs an output that is a JSON (or XML or some structured code) specifying that chart. For instance, it might output: { "component": "BarChart", "title": "Sales Q2", "data": [ ... ], "xAxis": "Region", "yAxis": "Revenue" }. This is essentially the LLM generating UI in textual description form.
Parsing the output: The application receives the LLM’s output. The front-end code is written to recognize structured outputs. If it sees a JSON with a component field, it knows the LLM is trying to render something. The app then maps this to actual UI. Using the above example, the app would call its BarChart component, pass the data and labels that the JSON provided, and render it for the user. If the LLM had instead returned just text, the app would render that normally in the chat or answer area.
Displaying the UI: Now the user sees not just a text answer but an actual interface element (in this case, a bar chart) that the LLM “requested.” The interface might also include multiple elements if the LLM output a list/array of components. The key is that the LLM’s response was structured in a way that the system could convert into visuals.

The ability to do this relies on training the LLM on examples of structured outputs or using techniques like OpenAI function calling (where the LLM can decide to call a function - e.g., “create_chart” - which returns UI data). Modern LLMs are surprisingly adept at producing JSON or code when properly guided. Developers often test prompts and adjust them to ensure the LLM reliably produces valid format (so it doesn’t hallucinate malformed JSON, for instance). Also, a validation layer is typically in place - if the LLM output is not parseable, the system can fall back or ask again.

In summary, an LLM can generate a UI from a prompt by outputting a description of the UI rather than a user-facing answer. That description is then rendered by the front-end application. This is exactly what tools like C1 by Thesys facilitate: they provide the models and specifications so that the LLM’s output corresponds to real UI components. It’s a fusion of NLP with front-end rendering. As a result, you can effectively “build UI with AI” - you prompt, the LLM outputs UI instructions, and your app creates the interface accordingly.

Q: Will AI-driven generative UIs replace front-end developers?
A: It’s better to say generative UIs will redefine the front-end developer’s role rather than outright replace it. AI-driven UI generation automates a lot of the routine work (laying out forms, creating standard views, etc.), which means front-end developers may not be coding those from scratch as much. However, there are several reasons front-end developers remain crucial:

Custom and Complex Design: AI can piece together known components, but human designers and developers still need to create the overall aesthetic, new custom components, and ensure a coherent design language. If anything, front-end devs will focus more on building the building blocks (components, design systems) and refining the AI’s outputs to meet high UX standards.
Guardrails and Quality: Developers will be needed to set up the rules for the AI and to handle edge cases. They’ll write the prompts or code that constrain the AI to produce good UIs (for example, making sure the AI doesn’t violate accessibility standards or company style guides). They’ll also handle integration of the UI with back-end logic and data securely.
Creative and Critical Thinking: While AI can generate UIs, it doesn’t inherently know what interface is best for user happiness or business goals beyond patterns it learned. Developers (often in collaboration with designers) will guide the overall user experience strategy. They might use AI as a tool (just like using a UI framework), but they still make the critical decisions on user flows, information hierarchy, etc. AI might suggest designs, but humans approve and fine-tune them.
Maintenance and Improvement: AI models might make mistakes or produce suboptimal UIs; developers will monitor user feedback and analytics to adjust how the generative UI behaves. It’s a continuous process of improvement - very much a developer’s job.
New Technical Skills: There’s also the emergence of new skills like prompt engineering for UI, and understanding how to mix code with AI outputs. Front-end devs will likely become specialists in orchestrating AI+code, which is a valuable skill set rather than an obsolete one.

In short, the role of front-end developers will evolve. They might write less boilerplate code and spend more time on high-level design logic, component development, and oversight of AI systems. Generative UI can be seen as another productivity tool - akin to how auto-layout didn’t eliminate web developers, or how game engines didn’t eliminate game programmers. It removes drudgery and frees developers to concentrate on the more interesting, complex problems. There will always be a need for human creativity and judgment in crafting user experiences. So, while fewer developers might be needed to achieve certain straightforward UI tasks (since AI can handle them), the demand for skilled front-end practitioners who can leverage AI will likely increase. They’ll be in charge of making sure AI-assembled interfaces are user-friendly, accessible, and aligned with product goals.

Early adopters in the industry often report that using generative UI tools cuts development time, but those developers then channel their time into other features or refinements rather than sitting idle. So, expect the front-end development landscape to change, but it’s more of a partnership with AI than a replacement. Human developers and AI will collaborate - the AI generating options or first drafts, and the developers guiding and perfecting the end result. This collaboration can lead to a faster, more iterative development cycle and likely more demand for developers who know how to work with these AI-driven systems. In summary, generative UI won’t replace front-end developers; it will empower them to build better interfaces faster, albeit with a shift in the nature of their day-to-day work.

Q: How can I get started with Generative UI in my own projects?
A: To get started with Generative UI, you can follow a few steps:

Explore available tools/frameworks: Look into platforms that support generative UI or AI-driven frontends. For example, C1 by Thesys API is one option that provides a ready-made solution for turning LLM outputs into UI. Other frameworks or SDKs (like Vercel’s AI SDK, LangChain with UI streaming, or open-source projects like llm-ui) might be suitable depending on your stack. Read their docs and see which fits your use case.
Integrate an LLM into your app: Ensure you have access to an LLM (like OpenAI GPT-4, etc.) either via API or open-source model. Generative UI needs an AI brain. Start with a simple setup: a chat interface or command in your app where you send user input to the LLM and get a response.
Define a structured output format: Decide how your LLM will output UI instructions. This could be JSON as discussed, or using a function-calling mechanism. For instance, you might define that the LLM can output an object with fields “component” and “props”. You’ll need to instruct the LLM accordingly. Write a few-shot prompt with examples. For example: User asks X… Response: { "component": "Text", "props": {"content": "…"} }. Provide a couple of scenarios (chart, table, etc.) in the prompt to teach the model.
Build or import a component library: If you use a tool like C1 by Thesys, this is provided. If not, you may need to have a set of React components (or Vue, etc.) that correspond to what the LLM might output. You can start simple: maybe just a Text component, Chart component (using a chart library), and a Form component. Ensure you can programmatically create these based on some data (e.g., if you have a Chart component that takes data prop, you can feed it data).
Write the rendering logic: In your frontend code, after you get the LLM’s response, parse it (JSON parse if text, or directly if using function calls). Then map that to your UI. For example, if response.component == "Chart", render <Chart ...props />. This part is like a switch or mapping function. If the LLM can return multiple components (like an array of component specs), loop through and render them in order.
Test with simple queries: Try some basic prompts to see if it’s working. Maybe ask the system (in your interface) a question that should return a chart. If it just gives text, iteratively improve your prompt. It might take tweaking to get the model to reliably output what you want. Using a smaller scope is fine: you can begin by focusing on one component type at a time.
Iterate on design and guardrails: As you get responses, you might notice the AI sometimes does odd things (maybe it suggests a component you didn’t implement, or the format is slightly off). Adjust your instructions to the model to avoid those. Also, start adding guardrails: for instance, validate that component is one of the allowed names, ignore or refuse anything else. Make sure malicious or buggy prompts can’t break the UI (e.g., the model shouldn’t output raw <script> tags or something - if using structured JSON this risk is low).
Expand and refine: Once the basics work, you can expand the set of components and the complexity. Perhaps incorporate tool use: e.g., if the LLM needs data to populate a chart, allow it to call an API or function that fetches data. This is advanced but powerful (this is how an AI might say “need weather data for SF” then display it). Also gather user feedback: do people like the AI-generated UI? Is it actually more useful? Use that to refine prompts or component behaviors.
Leverage documentation and community: Since Generative UI is a cutting-edge area, consult resources. Thesys has documentation on integrating C1 by Thesys (Bridging The Gap). LangChain’s examples on streaming UI or Vercel’s AI playground demos could give insight. Developer communities (GitHub, Discord, etc.) for generative UI are emerging - exchanging experiences can be valuable.
Start small in production: When ready to deploy a feature, you might start with a hybrid approach - e.g., a chatbot that sometimes shows a generative form. Gauge its stability. Over time, as confidence grows, you can offload more of your interface to the generative system. Always have a fallback for critical functions (for instance, if the AI fails to generate a needed input form, you should have a default form as backup).

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