AI Interfaces: From General Chatbots to Specialized Tools

As language models become increasingly integrated into our daily workflows, we find ourselves interacting with AI primarily through a single interface: the chatbot.

This format—flexible, open-ended, and conversational—has quickly become the default. But is it the most effective way for people to engage with powerful AI systems?

This paper explores that question by examining the limitations of the chatbot interface, its origins as a successor to search, and a potential future in which AI-powered tools become more specialized, contextual, and interface-driven.

Language Models as a New Layer of Search

At their core, large language models (LLMs) extend the function of search. Where traditional engines retrieved indexed documents, LLMs synthesize answers based on massive datasets. Instead of links, we now receive natural-sounding responses. Instead of keywords, we use plain language.

In that sense, LLMs represent a natural evolution in how we seek information—a new interface for an old task.

But while the underlying model has changed, the interaction paradigm has not. It still centers on inputting a question and receiving an output. The mechanism is richer, but the structure remains familiar.

The Constraints of the Chatbot Interface

Chatbots offer flexibility: a single field where users can ask anything. But that openness can also create friction, especially in tasks that require structure, iteration, or decision-making support.

For many, typing into a chat feels more like a command line than a user interface. While expressive, it lacks the affordances that graphical or task-specific interfaces provide—things like clear next steps, contextual feedback, or focused tools.

Chat interfaces are also cognitively demanding. Users must continuously frame their needs in language, recall previous prompts, and interpret abstract replies. These are not small barriers, especially for professionals or use cases with higher stakes.

Learning from the History of Software

If we look back, the evolution of software has always leaned toward specialization. We didn’t build one program to do everything—we built many. Tools for writing, tools for editing, tools for design, tools for therapy. Each tailored to a specific context.

And while these tools often shared underlying languages—such as code, markup, or query syntax—they were differentiated by their interfaces and intent.

LLMs today may be as foundational as programming languages once were. But just as we didn't ask users to write raw code for every task, we shouldn't expect users to prompt their way through every interaction.

A Shift Toward Domain-Specific AI Tools

A potential direction for the future of AI interaction is a move away from general-purpose chatbots toward domain-specific applications powered by language models. These tools would still use LLMs under the hood, but their design would be shaped by the particular needs of a field or task.

Examples might include:

• A mental health assistant grounded in a specific therapeutic framework

• A writing tool trained on a particular genre or tone

• A research assistant focused on summarizing scientific literature

Each tool would benefit from constraints—specific training data, a clear purpose, and a defined interface—making them more aligned with users' mental models and needs.

Personality, Perspective, and Partial Knowledge

Interestingly, one strength of specialization is that it allows for bias—not in the harmful sense, but in the sense of having a perspective.

Most tools, and indeed most humans, are useful not because they know everything, but because they know a subset of things well. They filter, frame, and prioritize based on their purpose.

LLM-based applications in the future might similarly adopt defined “points of view,” shaped by the data they're trained on or the users they serve. This could make them feel more aligned, more helpful, and in some cases, more trustworthy—not despite their partial knowledge, but because of it.

Toward a Broader Vocabulary of Interfaces

This shift—from generalized interfaces to contextual ones—suggests an opportunity for designers and HCI researchers.

Rather than centering all AI interaction in chat, we might explore new interface patterns that blend natural language with visual feedback, structured flows, or even ambient, screen-aware elements.

The goal isn’t to eliminate the chatbot—it has its place—but to recognize its limits and expand our design vocabulary for AI-powered tools.

Conclusion

As LLMs become more capable, the interfaces we build around them will shape how we use them. Today, the dominant pattern is the chatbot. Tomorrow, it may be something else: more structured, more specialized, and more integrated into domain-specific contexts.

The challenge—and the opportunity—is to design those future interfaces in ways that reflect how humans actually work, think, and create.