The excitement around large language models (LLMs) has led to many products with LLM-powered features. There are features like auto-complete, summarization, and natural language queries — integrated into otherwise conventional products. But most of these features are simply bolted onto legacy UX without rethinking the product from the ground up.
In this article, I’ll explore what it means to design a truly LLM-first product — where generative capabilities are not just features but the organizing principle of the user experience. I’ll contrast the status quo with native design approaches and lay out a framework for teams building the next generation of AI-native applications.
Note — I do not advise using a chat-based UI as the default. LLMs are more than just smarter chatbots.
Today’s typical LLM integrations are lightweight, assistive, and safe. They slot into existing workflows without changing the core logic or UX of a product. Examples:
These are all LLM-powered features — and they’re helpful. But they’re bolted onto existing patterns. They’re ultimately cosmetic. They don’t challenge or rethink how the product works, what it could do, or what the user’s role might become.
Even from a UI perspective, the user perceives their integration as a visual add-on — an additional button with evocative icons beside the traditional buttons of a given web app, or a feeble animated robot waving a hand in the bottom corner of a webpage trying to engage the user in a chat session to provide support.
Why is this the default? Because it’s low-risk and incremental. But it also treats the LLM as an advanced autocomplete or search engine — assistive, not transformative.
LLM-native products represent a fundamental historical occasion, conceptualizing software that requires more creativity to move beyond simply grafting AI capabilities onto existing paradigms. It means architecting the experience around natural language as the primary interface. Here, users express intent conversationally, and the LLM becomes a core system actor — not a helper, but a decision-maker.
These systems elevate AI from passive assistant to autonomous actor, capable of independent decision-making and complex task execution without constant human oversight.
Adopting an LLM-first approach triggers profound transformations in product development. In the following, I’ll briefly discuss how this new approach to application design impacts different phases/components of a complex system:
LLM-first systems are inherently uncertain — not just because users are unpredictable, but because the UI itself can invent new flows.
Traditional testing methods don’t account for this dual-sided creativity. It’s not enough to test static elements — you need to test behaviors that evolve dynamically and contextually. In a sense, the LLM becomes a co-designer of the interface, making conventional QA much harder — the users’ experience may be really variable, and the only way to limit the creativity of the the LLM is to cripple its “humanity” by lowering the temperature of the generation, that is the amount of randomness.
From a philosophical point of view, this is more than an engineering challenge. It reframes the entire relationship between humans and software — from operator-and-tool to intent-and-agent.
To start thinking LLM-first, ask:
Let’s take a simple and familiar use case. Employees use an internal expense reimbursement app to submit receipts, track approvals, and manage payouts. This app already exists in many companies in a conventional form, with forms, tables, dashboards, and rule-based workflows.
Here’s how that app might evolve across the Feature → Agent → Platform spectrum:
Here, the UI is structured around traditional input forms and status dashboards. Employees upload a receipt, fill in fields like amount, date, and category, and submit it. A manager reviews it in a queue.
LLM integration here is minimal and assistive. Maybe there’s a “suggest category” button next to the form field or an “explain this rejection” button that summarizes the policy.
The LLM helps, but only around the edges. The UI stays largely static and rule-driven.
The app introduces a goal-driven, partially autonomous assistant in the Agent phase.
Instead of filling out a form from scratch, the employee uploads a receipt or pastes an email with travel details, and the LLM extracts relevant fields, reasons through what’s missing, and prepares a submission draft. The agent may ask follow-up questions like “Was this for a client meeting?” to determine category or justification. On the approval side, the manager interface no longer just displays static requests but shows agent-generated summaries, suggested actions, or automated approval paths based on past behavior and thresholds.
The UI becomes more dynamic and conversational, with the LLM acting as an intermediary between human intent and system rules.
In the Platform phase, the concept of “submitting an expense” is no longer bound to form-based workflows. Employees can simply say, “I took a client to dinner yesterday,” and the system orchestrates the rest — retrieving the receipt from their email or card provider, matching the client to a CRM record, checking the per diem policy, and initiating the reimbursement.
The UI is built around intent capture, confirmation flows, and oversight. It’s no longer a dashboard for managing tickets, but a semantic layer for managing financial intent. Human interaction is centered around correction, validation, and training, not routine input.
In the figure below, I summarize how the UI evolves from the most traditional buttons and textboxes-based UI through the steps described above. The UI parts that are bound to the LLM are in green:
LLMs challenge long-held assumptions in product design. They shift interfaces from rigid workflows to adaptive, language-driven systems.
As we move beyond assistive features toward agentic and platform-level integrations, the role of the front end — and the product itself — begins to blur and evolve. The best LLM-first products will initially feel strange, even uncomfortable, because they break familiar patterns. But over time, they’ll reveal a new kind of simplicity — one that feels not just powerful but inevitable.
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