The New Role of Frontend Engineers in the Age of Generative UI

Meta Description:
Frontend engineering is evolving fast. Explore how Generative UI, LLMs, and AI-driven interfaces are reshaping developer skills, workflows, and team roles.

In the past, a frontend engineer’s job was clear-cut: build the user interface pixel by pixel, follow design specs, and make sure everything looked and felt right. Today, that role is evolving rapidly. We’re entering the age of Generative User Interfaces (GenUI) and how frontend engineering is evolving, what new skills are in demand, how workflows are shifting, and what it means for teams and careers in tech. The tone here is thoughtful and forward-looking

What Is Generative UI and Why Does It Matter?

Generative UI refers to interfaces that are dynamically generated by AI models (especially large language models, or LLMs) on the fly, rather than designed and coded entirely in advance. In a GenUI system, an application’s UI can adapt in real time based on user input, context, or intent, instead of being locked into a preset design. It’s like having a digital UX designer present for every user session, tailoring the layout and components to each user’s needs at that moment. Nielsen Norman Group 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,” which captures the essence of this paradigm shift (Bridging the Gap Between AI and UI: The Case for Generative Frontends).

This is a radical break from traditional frontends. In a conventional app, every user sees the same screens and forms unless a human developer releases an update. With generative UIs, the interface can partially design itself on demand. For example, imagine you’re using an AI-powered analytics tool. In a traditional app, you might get a fixed dashboard with a dozen charts, many of them irrelevant to your query. In a GenUI-driven app, if you ask a question about regional sales, the AI could generate a custom chart or table for that request, right then and there, instead of just returning text or making you sift through generic charts. If the AI needs more input from you, it could conjure a form with relevant fields automatically. Essentially, the UI becomes a conversation: the AI not only tells you information, it shows it to you in an optimal format. An LLM agent user interface might even assemble a full temporary dashboard to suit a complex query AI dashboard builder that creates a tailored analytics view with no manual setup. All of this happens in real time, driven by the AI’s understanding of your intent. In short, generative UI turns the frontend into a living, real-time adaptive UI that can morph as needed, rather than a static set of screens (AI Native Frontends). This makes the user experience far more intuitive and engaging, because the interface can adjust to the user instead of forcing the user to adjust to a one-size-fits-all interface.

Why does this matter so much? Because one of the biggest reasons AI projects fail to deliver value is the AI-UI gap (Bridging the Gap Between AI and UI: The Case for Generative Frontends). Users get frustrated or fail to see the value, and adoption suffers. In fact, after years of investment, 74% of companies have yet to see tangible value from their AI initiatives, and only 26% have managed to move beyond pilot projects to real impact (Bridging the Gap Between AI and UI: The Case for Generative Frontends). A key culprit is poor user adoption AI drive the interface, not just the back-end logic. As one tech consulting analysis put it, it’s time to stop patching legacy interfaces with token AI features and start building applications that are AI-native from the ground up. In AI-native software, the frontend is not an afterthought or a static shell AI-native user experiences that can keep up with the open-ended, dynamic nature of modern AI systems.

From CSS to Prompts: The Evolving Skill Set for Frontend Engineers

In the age of GenUI, the skill set of a frontend engineer is expanding. Traditionally, frontend developers have focused on skills like CSS layout, responsive design, JavaScript frameworks (React, Angular, etc.), and implementing static designs precisely as provided. Those skills are still important, but the emphasis is shifting. The frontend engineers of the near future need to be part developer, part AI wrangler.

Prompt engineering describing the desired outcome instead of explicitly building it. One frontend developer quipped that we might soon keep prompt files alongside our React components, not unlike how we keep test files today

Another emerging skill is LLM orchestration writing the logic around the AI

Crucially, frontend engineers will need to become adept in UI schema modeling and working with LLM UI components. Instead of coding a UI element by hand, the dev defines a schema or a set of component types that the AI can use. For instance, you might define that the AI can output a JSON object like {"component": "chart", "title": "...", "data": [...]} to request a chart component, or a {"component": "form", "fields": [...]} for a form. Designing and maintaining this “contract” (what components the AI can invoke and how it should specify them) is a new responsibility. It requires both technical and design insight: you’re effectively creating a mini design system or API for UI that the AI will use. The engineer must ensure this schema is robust, covers the needed use cases, and is easy for the model to use correctly. This is a blend of frontend knowledge and back-end API design, with a dash of prompt engineering to teach the AI to use the schema. It’s a very different kind of challenge from tweaking CSS, but it’s squarely in the frontend domain because it’s about how the UI is structured and presented.

It’s worth noting that traditional frontend skills don’t vanish guiding the AI (through prompts and schema) and by refining the components that the AI will use. Think of it like moving up a level of abstraction. Instead of manually crafting every interface, you’re crafting the system that crafts interfaces. This requires broader thinking and comfort with abstraction

New Workflows: Frontend Development Meets AI Orchestration

Generative UI doesn’t just change what skills frontend engineers need, it also changes how they work day to day. The development workflow for building an AI-driven, dynamic interface looks quite different from a traditional web project.

In a traditional workflow, you might start with fixed mockups from a designer, then implement those in code, creating various screens and states ahead of time. In an AI-native frontend workflow, much of that upfront hardcoding of screens can be skipped. Instead, the process often begins by defining the interface contract for the AI. The frontend engineer and team decide what components the AI can use and what the outputs should look like structurally. For example, if using an AI frontend API, you configure a system prompt that tells the model: “You can respond with these types of UI elements: tables, charts, forms, etc., in this JSON format.” You might provide sample outputs so the model learns the pattern. This is akin to writing a spec

Next comes building the integration and rendering layer. Rather than hardcoding a whole UI, the developer sets up a mechanism to take the model’s output and render it live in the app. If using a library or framework, this could be as straightforward as including a special component that handles the AI stream. In many cases, it involves writing a function that interprets the AI’s response. For instance, the app might receive a JSON from the LLM indicating a UI element; the frontend code needs to translate that into actual DOM elements or React components. This is where knowledge of your component library and state management comes in LLM outputs and actual UI components. Some frameworks have started providing this out-of-the-box: for example, open-source tools allow an AI agent to manipulate a React app’s UI directly, and platforms like C1 by Thesys provide a hosted API that streams UI component outputs which a React SDK can render (What Web Developers Must Know About Generative UI). By using such tools, a lot of the heavy lifting of connecting AI to UI is handled, letting developers focus on the logic and design of the interaction rather than low-level wiring.

During development, the workflow becomes more iterative and conversational. Instead of coding and refreshing a page to see a static UI, developers test prompts and AI outputs to see how the interface adapts. You might find yourself in the AI’s playground or console, trying out different user queries and refining how the model should respond. It’s almost like pair programming with the AI: you configure it, then ask it for something, see what UI it proposes, and then adjust either your prompt, schema, or component code to improve the result. This tight loop is a new kind of iterative design-development process. In some cases, frontend engineers work hand-in-hand with prompt engineers or data scientists to fine-tune the AI’s behavior. The line between “design” and “development” can blur

The role of testing and QA also changes. It’s not enough to check if a button works on different screen sizes; now you have to test how the AI behaves with various inputs and whether the UIs it generates are valid and pleasant. This can involve writing automated tests for AI outputs (like checking that the JSON schema from the model can be parsed without error) or doing scenario testing (e.g., “if the user asks for X, does the AI produce a reasonable interface?”). It’s a bit like testing a very flexible UI that has countless possible states. Frontend developers will likely develop libraries of prompt-response pairs as regression tests, and use techniques like vector similarity to ensure the model’s responses stay within acceptable bounds. These are new tricks in the frontend toolbox, marrying traditional web testing with AI validation. As one engineer noted, verifying AI responses requires different methods

One significant upside of these new workflows is speed and flexibility. Teams adopting generative UI approaches often find they can iterate on features much faster. Instead of spending weeks coding a new form or dialog, they can just update the AI’s prompt or add a new component type and the AI generates the rest on the fly. Frontend engineers save time on boilerplate and can focus on higher-level logic and refining user experience. Moreover, the UI can evolve continuously even after deployment. Need to support a new kind of interaction? Teach the AI with a prompt tweak or schema update, rather than rolling out an entirely new UI release. This leads to a more fluid development cycle where improvements can be pushed via AI configuration updates. It’s a shift toward frontend automation: letting the machine handle repetitive UI construction while developers supervise and guide the process. A recent industry article highlighted that everything from LLM UI components to AI dashboard builders are becoming practical, and tools like C1 by Thesys are making such frontend automation a reality (What Web Developers Must Know About Generative UI). The takeaway is that we’re moving beyond just automating code generation (like classic low-code tools) to automating the actual runtime assembly of interfaces. That’s a profound change in how software gets built.

Impact on Team Structure, Hiring, and Career Growth

As generative UI reshapes frontend development, it’s also poised to influence team structures and career paths in the tech industry. When parts of the UI can generate themselves, what does that mean for the people who build UIs?

One immediate impact is on collaboration between roles. The boundaries between frontend engineering, design, and product are likely to blur. In the era of static frontends, designers crafted pixel-perfect mockups and handed them to developers to implement. In an AI-driven frontend, some of that design work is done by the AI in real time

Interestingly, as AI tools democratize some aspects of design and development, some roles may shift in focus. Gartner predicts that by 2027 the number of UX designers in product teams will decrease by 40% due to AI-powered design automation, with many routine UX tasks being handled by developers or by AI itself (Gartner, 2024). That doesn’t necessarily mean companies will have 40% fewer people, but it means designers will concentrate on high-level experience strategy and complex research, while developers and product managers might take on more of the day-to-day interface tweaking with AI assistance. In fact, developers may end up doing more UX work than before, because AI tools enable non-designers to undertake certain UI design tasks with minimal training . This could manifest as product managers or engineers directly using AI UX tools to generate interface ideas or adjustments. Frontend engineers who are comfortable thinking about user experience will thrive in this scenario. They’ll be the ones to fill the gap when there are fewer traditional designers involved in the nitty-gritty of each interface change.

On the engineering hiring side, we’re already seeing demand for new hybrid skill sets. Job postings are beginning to ask for experience with prompts, familiarity with AI APIs, or the ability to integrate LLMs into applications. Titles like “AI Frontend Engineer” or “Full-Stack LLM Developer” are popping up. Companies are realizing they need people who can build and orchestrate AI-driven features. According to a recent McKinsey survey, organizations are not only retraining their existing developers to work with AI, but also hiring for new AI-related roles to accelerate their adoption of tools like generative UI. For frontend developers, this means it’s a perfect time to upskill. Those who add things like prompt engineering, basic understanding of machine learning, or experience with generative UI frameworks to their repertoire will stand out. It’s a chance to advance one’s career by riding the wave of a major technological shift. Rather than being threatened by AI, frontend engineers can position themselves as the indispensable bridge between cutting-edge AI capabilities and real-world user needs.

Team structures might also adapt to fully leverage generative UI. We may see “AI frontend” teams whose whole mandate is to create intelligent, adaptive interface components that other product teams can reuse. For example, an e-commerce company might have a small team developing a generative UI checkout flow that adapts to different user segments, and that team’s work is then plugged into all the company’s apps. This is similar to how some orgs have design systems teams full-stack orchestrator, dealing with UI, AI, and connecting APIs all at once. This could give rise to more multidisciplinary engineers.

From a career perspective, all of this is both exciting and challenging. For those willing to adapt, the growth opportunities are immense. We’re at the start of a new era in software interfaces; being one of the first engineers in your organization to champion and master generative UI is likely to get you a seat at the table for important projects. It’s a chance to move from implementing predetermined specs to actually influencing how products are conceived. After all, if the UI can do nearly anything given the right instructions, then deciding what it should do, when, and how becomes a product and engineering question rolled into one. Frontend engineers who embrace that broader scope will find themselves increasingly in leadership roles, shaping strategy as well as execution.

Of course, there will be a learning curve. Not every traditional frontend dev will immediately feel comfortable working with AI outputs or giving up some control over the exact UI details. But it’s worth noting that every major shift in frontend technology has brought similar challenges. Whether it was the move from table layouts to CSS, or from jQuery to component-based frameworks, those who learned the new approach early on were able to build the next generation of user experiences. Generative UI is likely to follow that pattern, albeit on a larger scale. The key message for frontend engineers is that AI is not here to replace you curator and orchestrator of interfaces, working alongside AI. Embracing that new role can be incredibly empowering. As Firestorm Consulting noted in a recent analysis, AI isn’t about making developers obsolete; it’s about freeing developers to focus on higher-level creative and logical challenges by offloading routine work to intelligent systems. In practical terms, that means frontend engineers can spend more time on what really matters

Conclusion

The rise of generative UI marks a turning point in frontend engineering. It’s transforming the job from one of painstakingly crafting static pages to one of choreographing dynamic, AI-driven experiences. Far from being a threat, this shift offers frontend developers a chance to amplify their impact and be at the forefront of building AI-native software. The role is becoming more challenging, yes, but also more creative and strategic. By mastering prompts, guiding AI behavior, and architecting flexible UI systems, frontend engineers will play a critical part in making AI-powered applications truly usable and useful. Businesses that recognize this and empower their teams to adopt generative UI will have a competitive edge

At Thesys, we’re dedicated to building the infrastructure for this new era of frontends. Thesys is the company pioneering AI frontend technology, and C1 by Thesys is our Generative UI API designed to help teams embrace these changes. C1 by Thesys lets you turn LLM outputs into live, interactive UI components, effectively automating the frontend. It’s an API layer that works with your LLM to generate interfaces on the fly (instead of returning just text), and a lightweight SDK that renders those interfaces in your application. In short, it enables your app’s UI to generate itself based on user input, context, or intent rather than being hardcoded. We built C1 by Thesys to make it easier for developers to create AI-native user experiences without reinventing the wheel each time. If you’re curious how it works and how it could fit into your stack, we invite you to explore our platform. Check out Thesys and the Thesys Documentation to see how live, adaptive UIs can be generated from LLM outputs with just a few lines of code. Don’t let your AI live in a static interface

References

• Firestorm Consulting. “Rise of AI Agents.” Firestorm Consulting, 14 June 2025.
• Gartner. (2024). Predicts 2025: Navigating the Rise of AI in Software Engineering. (Gartner research report).
• Boston Consulting Group. (2024). AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value. (Press release, 24 Oct 2024).
• McKinsey & Company. (2025). The state of AI: How organizations are rewiring to capture value. (McKinsey Global Survey on AI).
• Moran, Kate, and Sarah Gibbons. “Generative UI and Outcome-Oriented Design.” Nielsen Norman Group, 22 Mar. 2024.
• Firestorm Consulting. “The Evolution of AI App Interfaces: Past, Present, and Future.” Firestorm Consulting, 20 June 2025.
• Krill, Paul. “Thesys Introduces C1 to Launch the Era of Generative UI.” InfoWorld, 25 Apr. 2025.
• Firestorm Consulting. “Stop Patching, Start Building: Tech’s Future Runs on LLMs.” Firestorm Consulting, 14 June 2025.

FAQ

What is Generative UI, and why is it important for frontend developers?

Answer: Generative UI is a new approach to building user interfaces where the UI can be generated dynamically by AI (often by LLMs) rather than being fully pre-built by developers. It means the interface can change or build itself on the fly based on the user’s needs and context. This is important for frontend developers because it changes the way UIs are created. Instead of coding every element and interaction ahead of time, developers set up frameworks and prompts that allow an AI to assemble the UI in real time. The benefit is more adaptive, personalized interfaces for users AI-native product experiences. In essence, generative UI is important because it represents the future of frontends in AI-powered applications

Will generative AI replace frontend developers?

Answer: No change their role. AI can automate certain tasks (for instance, writing some UI code or generating layouts), but it still needs guidance, oversight, and integration by humans. Frontend engineers are the ones who set the rules for the AI, provide the building blocks (components/design system), and make sure the AI’s output makes sense in a real application. Generative AI is like a very powerful tool or co-pilot: it can take on the heavy lifting of UI generation, but a developer is still in charge of using that tool correctly. In fact, as more companies adopt AI-driven interfaces, the demand for developers who understand both frontend and AI will likely increase, not decrease. Someone needs to build and maintain the systems that let AI generate UI augment the role of frontend developers rather than make them obsolete.

What new skills do frontend engineers need in an AI-driven UI environment?

Answer: Frontend engineers will want to add a few key skills to thrive with AI-driven UIs. First, prompt engineering LLM orchestration and APIs is crucial. This means being comfortable with using AI services (like OpenAI, Anthropic, etc.), handling model responses (e.g., parsing JSON or function outputs from the AI), and managing things like errors or latency. Third, skills in defining UI schemas or component libraries for AI are important testing and validating AI output is a new skill: you’ll need strategies to ensure the AI’s UI suggestions are safe and user-friendly (for example, bounding what the AI can do, or checking its output against a schema). Finally, soft skills like collaboration and UX thinking become even more valuable. You’ll often be working closely with designers or product folks to fine-tune how the AI presents things, so being able to understand and influence user experience is key. In summary, a frontend engineer in the generative UI era should combine classic web development know-how with AI-specific skills like prompt design, AI integration, and a dash of data/ML literacy.

How do you actually generate a UI from a prompt using an LLM?

Answer: Generating a UI from a prompt using an LLM typically involves an AI frontend API or a similar mechanism where the LLM’s output is interpreted as UI instructions. Here’s how it works in practice: you start by giving the LLM a prompt (which could include the user’s request and some system guidance). For example, the system prompt might tell the LLM something like, “You are an assistant that can generate UI. You have components like table, form, chart available. When the user asks for something, respond with a JSON describing the UI.” Then, when a user prompt comes (say the user asks, “Show me a summary of sales by region”), the LLM will output a structured response instead of plain text { "component": "chart", "title": "Sales by Region", "data": [...] }. That output is essentially the AI saying, “I think the user needs a chart with this data.” The frontend application receives this and a special renderer or SDK on the frontend side takes that JSON and maps it to actual UI components (in our example, it would render a chart component with the provided data). Throughout this process, the developer’s job was to define the format of that JSON and ensure the LLM knows how to use it. Tools like C1 by Thesys are built to facilitate this ask the LLM for what you need in a structured way, the LLM returns a description of the UI, and your app turns that description into an actual interface on the screen. This allows real-time generation of interface elements driven by the AI’s logic and the user’s requests, making the application interface dynamic and context-aware.