20 August 2024

This experiment explores how AI can be used to create user interfaces (UI) that change and adapt based on what the user needs in real time. Instead of a static interface that looks the same for everyone, this system builds and adjusts UI components on-the-fly, offering a more personalized and engaging experience.

The AI behind this system does more than just tweak existing elements—it can actually create new components as needed, responding dynamically to how the user interacts with the interface. To make this work, the system requires more than just a standard AI model; it needs a well-trained and responsive AI that can make quick decisions and updates in real time.

We will test this approach in different scenarios, like online shopping, dashboards, and customer support, comparing the performance of these dynamic interfaces with traditional, static ones to see which offers a better user experience.

Case Study: Ecommerce Store

Imagine visiting an online store, but instead of navigating through endless menus and categories, you simply start by typing or speaking a query, like “I’m looking for a pair of running shoes under $100.”

The AI-driven interface immediately responds by presenting you with a tailored selection of options, such as filters for size, brand, and color, all embedded within a clean, minimalistic UI that’s designed just for your specific query.

As you interact with these options, the AI continues the conversation, asking follow-up questions like, “Do you prefer a specific brand?” or “Would you like shoes with extra cushioning?” Based on your responses, the AI refines the search and presents the most relevant products in a visually appealing layout.

When you make a selection, the interface dynamically adjusts again, this time guiding you through additional choices like related accessories or suggesting a size guide based on your purchase history. Throughout this process, the website feels less like a static page and more like a personalized shopping assistant, continually adapting to meet your needs and preferences in real time.

In this scenario, the AI doesn’t just present information—it creates a dynamic, interactive experience that feels conversational and intuitive, reducing the time and effort you spend searching for the perfect product.

Note: As pointed out by my friend Avijit:

"This solution effectively helps users quickly find what they’re looking for. However, what happens if the user isn’t sure what they’re searching for?"

We'll eventually address this and see how it fits into the broader scheme of things.

Methodology

1. AI Engine Development

Though agentic systems are pretty smart, in production, it is very important to ground them. These groundings can range anywhere from documents, to vector indexes, to API definitions, to components.

In our case, rather than generating the entire component using AI, we'll use AI primarily to customize existing components and manipulate the DOM to update its state.

So the tasks here to implement an AI engine capable of:

• Analyzing user queries, preferences, and behavior.
• customizing the appropriate pre-built components.
• Updating the DOM state and adding the component to the state

2. Component Library Creation

Next, we need to build out the set of components that the AI will refer to, and customize. So:

• Develop a library of pre-built UI components that can be dynamically arranged and customized.
• Components may include:
  • Navigation menus
  • Forms
  • Buttons
  • Information cards
  • Search bars
  • Widgets for specific tasks (e.g., calendar, task manager)
• Ensure that components are modular and flexible for easy adaptation by the AI.

3. User Experience Orchestration

Next, its time to put it all together with the help of an orchestration mechanism. This will be responsible for maintaining the state of the conversation with the user, taking the appropriate action based on the user's input, and repeating this in a cyclic manner till the user's objective is completed. LangGraph is a very good orchestration framework that can do this task well.

4. Prototype Implementation

Till now, we've done things in an experimental manner, stringing different pieces of technology together. Our main focus has been to make the idea work. Now, it is time to convert this into an initial prototype that can be consumed by the user.

• Build the prototype using modern front-end frameworks (e.g., React, Vue) integrated with the AI engine.
• Use APIs and data storage to handle user interactions, preferences, and AI-generated configurations.

7. Iterative Refinement

• Based on user feedback and testing data, iteratively improve the AI engine and component library.
• Experiment with more complex scenarios and a wider range of user needs.