Introduction

The success or failure of AI-powered applications often comes down to user experience. According to a Boston Consulting Group study, 74% of companies have yet to see tangible value from their AI initiatives, largely due to poor user adoption. In other words, even the most advanced model can flop if presented through a confusing interface. Early builders of AI-native software learned this firsthand and took a different approach. The breakthrough success of ChatGPT showed that a simple, intuitive UI (a friendly chat box) could unlock massive adoption of a sophisticated language model (Bridging the Gap Between AI and UI: The Case for Generative Frontends). In fact, ChatGPT’s easy conversational interface helped it reach over 100 million users within two months of launch – the fastest-growing app in history (Hu, 2023). By turning a complex AI into an everyday tool, its creators demonstrated how thoughtful UX can democratize cutting-edge tech.

AI-native software refers to applications designed from the ground up to leverage AI, and these demand interfaces as adaptive and intelligent as their AI back-ends. Early AI product teams realized that success meant rethinking traditional UX patterns. They pioneered new Generative UI (GenUI) approaches that make interfaces dynamic, context-aware, and user-centric. In this article, we’ll explore the best practices those early builders got right – from embracing natural language interaction to building trust and transparency – and how these lessons point toward the future of AI user interfaces. The goal is to balance insights from internal development (what product teams learned while building AI UIs) with external patterns that users found most intuitive. Let’s dive into the key principles that can elevate an AI UI from merely functional to truly delightful.

Embracing Natural, Conversational Interfaces

One of the biggest shifts early AI applications made was adopting natural language as a primary interface. Traditional software often relies on clicking menus or filling forms, but AI-native tools like ChatGPT proved that letting users express intent in plain language can dramatically lower the barrier to entry. A chat-based generative user interface feels approachable because it meets users on human terms. Instead of learning special commands, users simply ask questions or give instructions as if conversing with a colleague. This conversational paradigm turned advanced AI into something anyone could use. Early builders recognized that conversation could serve as the backbone of an AI UX, allowing the system to dynamically interpret requests and respond in kind.

Beyond text chat, natural language interfaces appear in voice assistants and other modalities, but the underlying principle is the same: make interaction as human-friendly as possible. These pioneers also ensured their UIs maintained context over multiple turns. For example, a user could ask, “Summarize this report,” then follow up with “Now make it focus on revenue,” and the AI remembers the context. Preserving dialogue history and user context was a best practice that early AI apps got right, enabling more fluid and powerful interactions. It made the AI feel more like a smart collaborator than a one-shot tool. In effect, conversation became the new navigation. Users could achieve complex outcomes by simply having a back-and-forth with the system, rather than clicking through rigid workflows.

Crucially, early teams learned that a conversational UI must also guide the user. Many people weren’t immediately experts at prompt design or unsure what to ask an AI. So successful AI apps embedded guidance into the interface. This included providing example prompts or suggested questions (as ChatGPT famously did on its home screen), tooltips with usage tips, and placeholder text in input boxes that hinted at capabilities. By level-setting the experience and educating users within the UI, these products helped users get better results. Google’s UX researchers have noted that new users of generative AI often don’t know where to start; the best AI UIs solve this by demonstrating possibilities up front. For instance, Midjourney’s prompt examples or Notion AI’s template suggestions show users “here’s what you can ask me to do.” By embracing natural language and adding gentle onboarding cues, early AI-native software made powerful technology feel accessible as I discuss in What is Generative UI.

Guiding Users with Context and Memory

Another practice early AI product teams got right was designing interfaces that keep the AI and the user on the same page. Context retention and smart use of memory in the UI flow turned out to be essential for good UX. Unlike traditional apps where each action might be isolated, AI systems often need to understand the user’s intent over a sequence of interactions. Pioneering applications tackled this by surfacing context cues in the interface. For example, chat-based tools display the conversation history so users (and the AI) can refer back to earlier messages. This running context makes the experience feel coherent and reduces the need for users to repeat themselves. It also signals to users that the AI “remembers” what has been said, encouraging a more conversational style of use.

Early builders also implemented goal-oriented UI flows to help users and AI stay aligned. If an AI assistant needed more information to fulfill a request, the interface would proactively ask a follow-up question rather than returning an error or a generic failure. This was a major upgrade from legacy chatbots that often hit dead-ends. For instance, if a user’s prompt was ambiguous or too broad, a well-designed AI UI might respond with clarifying options: “I’m not sure what you need – do you want a summary or a detailed analysis?” Offering these guided follow-ups (often via quick reply buttons or multiple-choice prompts) shortened the path to a useful answer. Early conversational assistants proved the value of treating follow-up as a default response, rather than leaving the user to figure out the next step on their own. This pattern showcased the AI’s understanding and kept the dialogue flowing toward the user’s goal.

Maintaining context wasn’t just about conversation history – it also extended to user-specific information and preferences. Some AI-native software began to incorporate personalization, storing user settings or past behaviors to inform future interactions. For example, an AI writing tool might remember a user’s preferred tone or an AI data assistant might recall which dataset a user frequently references. By persisting relevant context, these interfaces could anticipate needs and avoid making the user repeat known information. Early adopters of this strategy found it boosted efficiency and made the AI feel more attuned to the individual user. It’s an insight from internal development: enabling a bit of memory and state in the UI/AI layer can greatly enhance usability. Users came to see the AI not as a reset-every-time bot, but as an assistant that learns and adapts. This practice of guiding users and leveraging context is now a cornerstone of AI UX design, ensuring interactions are coherent, efficient, and tailored.

Building Trust Through Transparency and Control

Trust is paramount in AI interactions, and early AI software builders understood that how an AI’s output is presented can make or break user confidence. A key best practice was being transparent about AI involvement and limitations. Rather than hiding the fact that an AI is behind the scenes, successful interfaces clearly “broadcast the use of AI” (WillowTree’s 7 UX/UI Rules…). Users need to know they’re interacting with an AI assistant (not a human) so they can calibrate their expectations accordingly. Early on, this meant simple measures like the assistant referring to itself in the first person as a tool (e.g., “As an AI, I’ll try to help with that…”), or using an AI icon or name that signals its automated nature. The interface might also explicitly clarify what the AI can or cannot do. By level-setting expectations up front, these UIs avoided the pitfall of users feeling misled or expecting magic. In turn, users were more forgiving of mistakes and more willing to engage, because the system was honest about being an AI.

Another aspect of transparency is showing why the AI responds the way it does. Early AI-native apps discovered that users trust the system more when they can see some evidence or source behind an answer. For instance, Bing’s AI chat and other search-based assistants started including footnotes or citation links in their responses to indicate the source of facts. This practice of using source links as markers of trust reassures users that the AI isn’t just making things up – they can verify information if they choose. Similarly, some interfaces label AI-generated content with subtle indicators (like a different color text or an icon) to differentiate it from user inputs or factual data. The best designs made these transparency features visible but unobtrusive, so they inform the curious without overwhelming the average user. Early adopters of this approach found it significantly increased user confidence in AI outputs.

Importantly, transparency goes hand-in-hand with giving users control and safety valves when using AI. Recognizing that AI is not infallible, early UX designers built in features for oversight. A common pattern was the inclusion of feedback mechanisms – for example, a thumbs up/down on each AI response or a quick “Was this helpful?” prompt. This not only engaged users in refining the AI’s performance, but also provided an outlet if something went wrong. Users could downvote a bad answer and often the system would then apologize or try again, demonstrating a form of accountability. Additionally, when AI systems had the ability to take actions (like executing code, sending messages, or making purchases), early builders wisely kept the human in the loop. The UI would require a user confirmation (a clear Allow or Cancel step) before any high-stakes action was carried out, ensuring that the user remained in ultimate control. These measures reflected an internal strategy of “guardrails via UX” – using the interface to enforce ethical and safe AI operation.

Finally, transparency in AI UX also meant being upfront about errors or uncertainties. Instead of generic error codes, AI-native interfaces would say things like, “I’m sorry, I couldn’t find enough information on that topic.” Some even offered an explanation: e.g., “I only have data up to 2021, so I might not know about recent events.” By candidly acknowledging limitations, the interface built trust through honesty. This approach aligns with the broader best practice of always aiming for transparency, as it turns inevitable AI shortcomings into moments to educate the user. In summary, early AI software got it right by designing for trust: clearly identifying the system as AI, revealing its sources and thought process where appropriate, and empowering the user with feedback and control. These practices transformed AI from a mysterious black box into a cooperative partner in the user’s eyes.

Iterative Feedback and Adaptive Interaction Loops

AI-native applications thrive on iteration – not just improving the model behind the scenes, but enabling interactive iteration in real time with users. Early builders discovered that a hallmark of good AI UX is facilitating a tight feedback loop between the user and the system. Instead of treating each query or command as a one-and-done transaction, the best interfaces encouraged users to refine and elaborate. For example, after an AI agent produces an answer or output, the UI might proactively ask, “Did this solve your problem, or do you want to adjust something?” This simple prompt invites users to give quick feedback (like hitting a thumbs-down if the answer missed the mark) and immediately triggers the AI to respond to that feedback. Users found this dynamic incredibly powerful – it was no longer, “sorry, try again from scratch,” but rather, “I hear you, let’s improve this together.” Early adopters of such quick feedback collection noticed higher user satisfaction, because people felt the system was learning with them.

One effective pattern was letting users correct the AI in context. In a coding assistant, for instance, if the suggested code didn’t work, the interface would let the user say, “That output had an error, please fix it,” rather than making them re-describe the whole problem. This kind of iterative prompt capability turned AI into a cooperative problem-solving experience. The LLM-driven product interface essentially became a continuous conversation where each turn could build on the last – much closer to how a human assistant would work. It required the UI to handle multi-turn interactions gracefully, updating outputs on the fly. Many early AI tools implemented features like an “Edit query” or “Refine” button, allowing users to tweak their last prompt and resubmit quickly. This encouraged experimentation and learning by doing, which users appreciated in mastering how to get the best results from the AI.

Adaptive front-end automation also played a role here. Some teams built their interfaces to automatically adjust based on user feedback patterns. For example, if many users kept asking a writing AI to “make it more formal,” the product team might introduce a tone slider or preset buttons (“More Formal”, “More Casual”) in the UI for future iterations. In this way, the UX literally evolved through usage data – an agile approach to design that early AI-native products excelled at. Internally, this was a strategy of instrumenting the UI to capture where the AI wasn’t meeting user needs, then rapidly updating the interface to fill the gaps (often without needing a full app update, if the UI was partially generated by the AI). This kind of adaptability exemplifies a core advantage of generative interfaces: because parts of the UI can be dynamic, you can respond to user behavior in real time. In practical terms, users saw interfaces that felt increasingly personalized and responsive the more they interacted as I discuss in What is Generative UI.

Additionally, early builders began experimenting with multi-modal feedback – letting users give input or get output in different forms depending on what’s effective. A notable best practice was providing alternatives to typing when possible: for instance, voice input for speaking a prompt, or image upload for a query about a diagram. Conversely, if an AI could output a chart or a table to make the answer clearer, the UI would show that instead of a long text paragraph. These adaptations often happened in iterative fashion: an initial response might be text, but if the user asks for clarification or a different view, the AI might generate a graph next. The interface’s ability to fluidly switch mediums (text, visuals, interactive widgets) based on user feedback made the experience feel richer and more real-time adaptive. Users weren’t stuck with one format – they could nudge the AI to present information in the way that made sense to them, all within the same conversation or session.

In summary, early AI-native software got iterative interaction right by treating each user query as the start of a dialogue, not the end. Through immediate feedback tools, refine options, and adaptive UI responses, these systems created a sense of collaboration. The UX became less about one perfect answer and more about converging on the best result through interaction. This not only improved outcomes but also engaged users, who felt heard and involved. It’s a virtuous cycle: the easier it was to iterate with the AI, the more users did so, generating data that further improved the system. Modern AI UX continues to build on this lesson – that an interactive loop beats a one-shot attempt, especially when dealing with complex or creative tasks.

Dynamic and Context-Aware UI Elements

Perhaps the most forward-thinking practice among early AI software builders was the idea that an application’s interface should adapt itself to fit the user’s needs in the moment. Traditionally, an app’s UI is static – every user gets the same screens and options. But AI-native thinking turned that on its head. If the AI can understand what a user is trying to do, why not have it generate the interface that best presents the information or options for that situation? I discuss this in What is Generative UI that this gave rise to the concept of Generative UI: interfaces that build themselves on the fly using AI. Early glimpses of this appeared when AI apps would alter their output format or offer special UI components depending on the query. For example, if you asked an AI data assistant “compare sales of Product A vs Product B,” a naive system might just reply with a text paragraph. But an AI UI following best practices would realize this is better shown as a chart and actually display a chart comparing A and B. The interface essentially redesigned itself to give the user a more effective answer (What is Generative UI). This level of adaptivity delighted users, because the app felt smart and context-aware, not one-size-fits-all.

Internally, teams learned that achieving dynamic UIs required building blocks that an AI could manipulate. These are often referred to as LLM UI components – standardized widgets like tables, charts, forms, or buttons that a language model can summon through its output. Pioneering projects developed schemas or markup (for instance, using JSON or special tags in the LLM’s response) that the frontend could translate into live UI elements. A simple example is an AI writing assistant that, when asked to draft an email, not only outputs the text but also provides a “Send” button or editable fields right in the interface. That button is an LLM-driven UI component generated because the model’s answer included an instruction to create it. Early builders who implemented such components enabled a new level of interactivity: users could trigger actions or tweak parameters from the AI’s output itself, rather than copying and pasting into another tool. This LLM agent user interface approach, where the AI can effectively modify the app UI, opened the door to far richer experiences. Users could have, say, an AI-generated form to fill in additional details after a request, or interactive maps rendered when asking about geographic data – all appearing spontaneously when needed.

A related best practice was focusing on outcome-oriented design. AI UIs that got this right would ask: what is the user’s actual goal, and how can the interface display the answer in the most usable form? Sometimes that meant bypassing lengthy AI explanations in favor of a direct visual or a concise result. For instance, an AI dashboard builder for business intelligence might dynamically generate a custom dashboard view when a user queries financial metrics, rather than just listing numbers. By tailoring the presentation (the UI) to the query, early AI software made it faster for users to grasp insights and take action. Users found this far more effective than static dashboards, as the interface would surface what mattered to them. One Thesys blog put it succinctly: prompt-to-UI tools can instantly turn a text description into code, but Generative UI “revolutionizes how people experience software by letting the interface shape itself in real time, uniquely for them.” (What is Generative UI). In practice, this meant the best AI apps felt like they knew when to show you a graph, when to let you click a button to drill deeper, or when to present multiple options vs a single answer.

Of course, creating a dynamic UI that changes per user also introduced challenges. Early teams had to ensure that these auto-generated interface elements still felt cohesive and professional. They addressed this by establishing a consistent design language for all AI-generated components. Essentially, they gave the AI a toolkit of front-end elements styled to match the app’s look and feel, so that whether the AI showed a chart or a form, it looked like it belonged. Keeping a consistent look and feel was important to avoid confusing users with UIs that changed too drastically. Done right, users might not even realize parts of the interface were being generated on the fly – it just felt like the app anticipated their needs. Another internal insight was to impose negotiable constraints: the AI could shape the UI, but within bounds that designers set (to prevent, say, nonsensical layouts). This mix of AI-driven flexibility and designer oversight let early products safely explore dynamic interfaces.

The benefits of these adaptive interfaces soon became clear. Users engaged more when the UI responded to their context – it gave a sense of a personalized experience. In enterprise settings, such UIs meant each user or team could essentially get a custom application behavior without additional development. For example, a sales manager might see the AI interface emphasize CRM data, while an engineer using the same tool might see code snippets or diagrams, all generated by the AI understanding who is asking what. These real-time adaptive UIs turned out to be the missing piece in many AI solutions that previously felt disconnected. As a result, forward-thinking companies started crafting a frontend for AI agents that could visualize agent decision steps, show interim results, or offer interactive controls to steer the agent – far beyond the static chat box. Early builders who embraced this dynamic UI philosophy validated that when an interface adapts to the user’s request and context, it vastly improves effectiveness and user satisfaction. As I discuss in What is Generative UI, this laid the groundwork for modern GenUI platforms like C1 by Thesys, which now provide an AI frontend API for developers to easily generate live, interactive UIs from LLM outputs. The lesson from those early days is clear: a responsive interface that can change with the problem at hand isn’t just nice-to-have, it’s essential for unlocking the full potential of AI in software.

Conclusion

The early builders of AI-native software got a lot right. They realized that powerful algorithms alone aren’t enough – the real magic happens when advanced AI is wrapped in an intuitive, user-centric experience. By learning from both successful launches and hard-earned lessons, these pioneers established core UX principles for the AI era. They showed that natural language Generative UI can make complex technology accessible to anyone. They demonstrated the importance of guiding users with context and providing adaptive help along the way. They put transparency and user control at the forefront to build trust in AI systems. And they pushed the envelope with dynamic interfaces that break out of the static mold, proving that software doesn’t have to be one-size-fits-all. Each best practice we’ve discussed – conversational design, guided interactions, transparency, feedback loops, and adaptive UI – contributed to turning novel AI tech into practical tools people love to use.

As AI continues to evolve, these UX lessons remain incredibly relevant. Today’s teams are expanding on these ideas, from designing LLM-driven product interfaces that can change on demand to creating AI UX tools that help non-designers craft better AI interactions. The path that early builders charted is now helping a much wider audience build AI features and apps that feel truly engaging. In a sense, those first successes set user expectations: we now expect AI assistants to be friendly, helpful, and smart in how they present information. Meeting those expectations will require the next generation of AI products to double down on UX innovation. Fortunately, the blueprint is there – thanks to the early innovators who showed what works. By applying these best practices, modern developers and designers can create AI experiences that not only wow users with intelligence, but also feel seamless, trustworthy, and even delightful to use.

Thesys: Pioneering the Future of Generative UI

Thesys is a company building the infrastructure to bring these AI UX best practices to life at scale. Focused on AI frontend innovation, Thesys offers cutting-edge tools for creating dynamic UI with LLM capabilities. Its flagship product, C1 by Thesys, is the world’s first Generative UI API – a platform that empowers developers to build UI with AI. With C1, an LLM’s outputs aren’t just text; they can become live charts, forms, buttons, and more, assembled into an interface on the fly. This means real-time, adaptive user experiences without months of hand-coding. From AI dashboard builders to intelligent assistants, Thesys enables AI tools to generate rich, interactive UIs directly from a prompt. It’s the next step in frontend automation, turning AI agent reasoning into tangible interface elements instantly. To learn more about how Thesys is redefining what’s possible in LLM-driven product interfaces, visit thesys.dev and explore the C1 API documentation at docs.thesys.dev. With Thesys, teams can transform LLM outputs into intuitive apps – unleashing the full potential of AI through great UX.

References

Boston Consulting Group. (2024). AI Adoption in 2024: 74% of companies struggle to achieve and scale value. Press release.

Hu, K. (2023). ChatGPT sets record for fastest-growing user base. Reuters.

Moran, K., & Gibbons, S. (2024). Generative UI and Outcome-Oriented Design. Nielsen Norman Group.

Couldwell, D. (2025). Building generative AI? Get ready for generative UI. InfoWorld.

Rindani, S. (2023). WillowTree’s 7 UX/UI Rules for Designing a Conversational AI Assistant. WillowTree Insights.

AI UX Best Practices: What Early Builders of AI-Native Software Got Right

AI UX Best Practices: What Early Builders of AI-Native Software Got Right

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