AI Copilots in React: Building Chat-Powered UIs with Vercel's Free SDK

Ralph Sanchez

Frontend Engineer

UI Designer

React Native Developer

AI Engineer

React

Vercel

Next.js

TypeScript

Computer Software

AI Copilots in React: Building Chat-Powered UIs with Vercel's Free SDK

The era of static, unresponsive interfaces is over. Today's users expect more than just clicking buttons and filling out forms. They want to have conversations with their apps, ask questions in plain English, and get intelligent responses that actually help them get things done. This guide will show you how to build exactly that: AI copilots embedded directly in your React applications using Vercel's free and powerful AI SDK.

This technology isn't just a cool feature anymore. It's becoming the standard for modern applications. Building an AI-powered UI is a significant step forward, and it needs a rock-solid foundation. Make sure your application loads lightning fast by using advanced image component tricks for Core Web Vitals. If you're building for e-commerce, you can spin up a headless store in a day with the right approach. And when you're ready to scale your AI-powered applications, you can hire Next.js developers who understand this cutting-edge technology.

The Rise of Conversational UI: Why Now?

We're at a turning point in how people interact with software. The traditional approach of navigating through menus, clicking buttons, and filling out forms is starting to feel outdated. Users now expect to communicate with applications the same way they talk to friends or colleagues. They want to ask questions, describe what they need, and get intelligent responses that actually solve their problems.

This shift isn't happening by accident. Recent breakthroughs in Large Language Models have made it possible to create AI assistants that understand context, remember conversations, and provide genuinely helpful responses. What used to require a team of engineers and months of development can now be built in days with the right tools.

Moving Beyond Traditional Forms and Buttons

Think about the last time you used a complex software application. Maybe you were trying to generate a report in your analytics dashboard. You probably had to click through multiple menus, select date ranges from dropdowns, choose filters from lists, and finally hit a "Generate" button. It works, but it's not exactly intuitive.

Now imagine if you could just type: "Show me last month's sales data for our top 10 products." The AI understands what you want and generates the report instantly. No hunting through menus. No remembering where that specific feature lives. Just a simple conversation that gets you what you need.

This conversational approach doesn't just save time. It fundamentally changes how people feel about using your application. Instead of learning a complex interface, they can use natural language they already know. New users can get started immediately without tutorials or training. Power users can accomplish complex tasks faster than ever before.

Use Cases for AI Copilots

The possibilities for AI copilots extend far beyond simple chatbots. Let me share some real examples that show the true potential of this technology.

In SaaS applications, an AI copilot can act as an always-available expert. Users can ask questions like "How do I set up automated billing?" or "What's the best way to organize my team's projects?" The copilot doesn't just point to documentation. It provides step-by-step guidance tailored to the user's specific situation.

For e-commerce sites, imagine a shopping assistant that actually understands what customers want. A user types: "I need a waterproof jacket for hiking in Scotland this fall, budget around $200." The AI doesn't just search for "waterproof jacket." It considers the climate, the activity, the budget, and returns personalized recommendations with explanations for each choice.

Data dashboards become incredibly powerful with conversational interfaces. Instead of building complex queries, users can ask: "Which marketing campaigns had the highest ROI last quarter?" or "Show me customer churn trends broken down by subscription tier." The AI translates these natural language queries into the appropriate data visualizations.

Even creative tools benefit from AI copilots. A design application might let users say: "Make the background more vibrant but keep it professional" or "Add some whitespace between these elements." The AI understands design principles and applies them intelligently.

Introducing the Vercel AI SDK: Your Toolkit for Building with AI

Building these conversational interfaces from scratch would be a massive undertaking. You'd need to handle streaming responses, manage conversation state, deal with error handling, and create all the UI components. That's where the Vercel AI SDK comes in. It's an open-source library that handles all the complex parts, letting you focus on creating amazing user experiences.

What is the Vercel AI SDK?

The Vercel AI SDK is like having a senior engineer on your team who's already solved all the hard problems of building AI-powered interfaces. It provides React hooks and components that handle the intricate details of communicating with AI models, managing conversation state, and rendering responses in real-time.

What makes this SDK special is that it's designed specifically for modern web applications. It's not a generic AI library that you have to wrestle into working with React. Every feature, every API, every component is built with React and Next.js developers in mind. You write familiar React code, and the SDK handles all the AI complexity behind the scenes.

The SDK is also provider-agnostic. Whether you're using OpenAI, Anthropic, Google's models, or even open-source alternatives, the SDK provides a consistent interface. You can switch between providers or use multiple models in the same application without rewriting your code.

Key Features and Benefits

The Vercel AI SDK comes packed with features that make building AI applications surprisingly straightforward. Here's what sets it apart:

Streaming-first architecture means your users see responses appear word by word, just like ChatGPT. This creates a much more engaging experience than waiting for complete responses.

Built-in React hooks like useChat and useCompletion manage all the complexity of conversation state. You don't need to write reducers, handle API calls, or manage loading states. It's all done for you.

Edge runtime support lets you deploy your AI features globally with minimal latency. Your copilot responds quickly whether your users are in New York or Tokyo.

Type safety throughout the entire stack means you catch errors during development, not in production. The SDK provides full TypeScript support with intelligent autocomplete.

Automatic error handling and retries ensure your AI features stay reliable even when APIs have hiccups. The SDK handles transient failures gracefully without you writing a single try-catch block.

Why 'Streaming-First' Matters

Let me explain why streaming is such a game-changer for AI interfaces. Traditional APIs work on a request-response model. You send a request, wait for the server to process everything, then get the complete response. With AI models generating lengthy responses, this could mean waiting 5-10 seconds staring at a loading spinner.

Streaming changes everything. As soon as the AI model starts generating tokens, they're sent to your application. Users see the response building up word by word, creating a sense of immediacy and engagement. It feels like the AI is thinking and typing in real-time.

This isn't just about perceived performance. Streaming allows users to start reading and processing information immediately. They can even interrupt if the response is going in the wrong direction, saving time and API costs. It transforms the interaction from a static query-response pattern into a dynamic conversation.

The Vercel AI SDK makes streaming trivial to implement. What would normally require WebSockets, chunked transfer encoding, and complex state management is reduced to a single hook. You get ChatGPT-like streaming with just a few lines of code.

Building Your First Chatbot: A Step-by-Step Guide

Let's get practical and build a real AI copilot. We'll create a chat interface in a Next.js application that can have intelligent conversations with users. Don't worry if this seems complex - the Vercel AI SDK makes it surprisingly simple.

Setting Up the Backend: The API Route

First, we need to create an API endpoint that will handle communication with the AI model. In Next.js, we'll create a new file at app/api/chat/route.ts:

import { OpenAI } from 'openai';
import { OpenAIStream, StreamingTextResponse } from 'ai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

export async function POST(req: Request) {
  const { messages } = await req.json();

  const response = await openai.chat.completions.create({
    model: 'gpt-3.5-turbo',
    stream: true,
    messages,
  });

  const stream = OpenAIStream(response);
  return new StreamingTextResponse(stream);
}

That's it. In just 15 lines of code, we've created a streaming AI endpoint. The OpenAIStream helper converts OpenAI's response format into a standard stream that works perfectly with the frontend SDK.

Notice how we're not handling any of the streaming complexity ourselves. No chunking, no parsing, no manual response building. The SDK handles all of that, letting us focus on the actual AI integration.

Creating the Frontend: The `useChat` Hook

Now for the magic part - the frontend. The useChat hook is where the Vercel AI SDK really shines. Create a new component:

'use client';

import { useChat } from 'ai/react';

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat();

  return (
    <div className="flex flex-col h-screen">
      <div className="flex-1 overflow-y-auto p-4">
        {messages.map(m => (
          <div key={m.id} className="mb-4">
            <div className="font-bold">
              {m.role === 'user' ? 'You: ' : 'AI: '}
            </div>
            <div>{m.content}</div>
          </div>
        ))}
      </div>
      
      <form onSubmit={handleSubmit} className="p-4 border-t">
        <input
          value={input}
          onChange={handleInputChange}
          placeholder="Say something..."
          className="w-full p-2 border rounded"
        />
      </form>
    </div>
  );
}

Look at how little code this requires. The useChat hook provides everything: the messages array, input state, change handlers, and submit function. It automatically calls our API endpoint, handles the streaming response, and updates the UI in real-time.

Rendering the UI

The beauty of the useChat hook is how it manages the entire conversation flow. Let's enhance our UI to make it more polished:

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat();

  return (
    <div className="flex flex-col h-screen max-w-2xl mx-auto">
      <div className="flex-1 overflow-y-auto p-4 space-y-4">
        {messages.map(m => (
          <div
            key={m.id}
            className={`flex ${m.role === 'user' ? 'justify-end' : 'justify-start'}`}
          >
            <div
              className={`max-w-xs lg:max-w-md px-4 py-2 rounded-lg ${
                m.role === 'user'
                  ? 'bg-blue-500 text-white'
                  : 'bg-gray-200 text-gray-800'
              }`}
            >
              {m.content}
            </div>
          </div>
        ))}
        {isLoading && (
          <div className="flex justify-start">
            <div className="bg-gray-200 text-gray-800 px-4 py-2 rounded-lg">
              <span className="animate-pulse">AI is thinking...</span>
            </div>
          </div>
        )}
      </div>
      
      <form onSubmit={handleSubmit} className="p-4 border-t bg-white">
        <div className="flex space-x-2">
          <input
            value={input}
            onChange={handleInputChange}
            placeholder="Ask me anything..."
            className="flex-1 p-2 border rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500"
            disabled={isLoading}
          />
          <button
            type="submit"
            disabled={isLoading}
            className="px-4 py-2 bg-blue-500 text-white rounded-lg hover:bg-blue-600 disabled:opacity-50"
          >
            Send
          </button>
        </div>
      </form>
    </div>
  );
}

This enhanced version includes loading states, better styling, and a more polished chat interface. The isLoading property from the hook lets us show feedback while the AI is processing, creating a smooth user experience.

Beyond Text: Generative UI with the Vercel AI SDK

Here's where things get really exciting. The Vercel AI SDK doesn't just handle text responses. It can generate entire React components on the fly based on the conversation context. This opens up possibilities that go far beyond traditional chatbots.

What is Generative UI?

Generative UI is a paradigm shift in how we think about AI responses. Instead of the AI returning just text, it can return instructions to render specific UI components with dynamic data. The AI becomes not just a conversationalist, but a UI designer that creates interfaces on demand.

Imagine asking your AI copilot: "Show me the sales performance for Q4." Instead of returning a text description, the AI could generate an interactive chart component. Or when a user asks about a specific product, the AI could render a product card with images, prices, and an add-to-cart button.

This approach combines the flexibility of natural language with the richness of custom UI components. Users get the best of both worlds: they can ask for anything in plain English, and receive purpose-built interfaces in response.

Example: A Dynamic Stock Ticker Component

Let's build a concrete example. We'll create a system where users can ask about stock prices, and the AI responds with live, interactive components:

First, define the stock ticker component:

function StockTicker({ symbol, price, change }: { 
  symbol: string; 
  price: number; 
  change: number;
}) {
  const isPositive = change >= 0;
  
  return (
    <div className="border rounded-lg p-4 bg-white shadow-sm">
      <div className="flex justify-between items-center">
        <h3 className="text-lg font-bold">{symbol}</h3>
        <div className={`text-2xl font-bold ${isPositive ? 'text-green-600' : 'text-red-600'}`}>
          ${price.toFixed(2)}
        </div>
      </div>
      <div className={`text-sm ${isPositive ? 'text-green-600' : 'text-red-600'}`}>
        {isPositive ? '▲' : '▼'} {Math.abs(change).toFixed(2)} ({(change / price * 100).toFixed(2)}%)
      </div>
    </div>
  );
}

Now, configure the AI to use this component in the API route:

import { render } from 'ai/rsc';

const response = await openai.chat.completions.create({
  model: 'gpt-4',
  messages,
  functions: [{
    name: 'show_stock_price',
    description: 'Display current stock price',
    parameters: {
      type: 'object',
      properties: {
        symbol: { type: 'string' },
        price: { type: 'number' },
        change: { type: 'number' }
      }
    }
  }]
});

// When the AI calls the function, render the component
if (response.choices[0].function_call?.name === 'show_stock_price') {
  const args = JSON.parse(response.choices[0].function_call.arguments);
  return render(<StockTicker {...args} />);
}

How it Works: The `render` Function

The render function is the bridge between AI decisions and React components. It serializes React components in a way that can be safely transmitted from the server and reconstructed on the client. This isn't just converting components to HTML - it preserves interactivity, state, and event handlers.

Security is built in from the ground up. You explicitly define which components the AI can render, preventing any malicious code injection. The AI can only use the components you've specifically allowed, with the props you've defined.

Here's a more complete example showing multiple components:

const components = {
  StockTicker,
  WeatherCard,
  TodoList,
  Chart
};

export async function POST(req: Request) {
  const { messages } = await req.json();
  
  // Let the AI know what components are available
  const systemPrompt = `You can render these UI components:
    - StockTicker: for showing stock prices
    - WeatherCard: for weather information
    - TodoList: for task management
    - Chart: for data visualization
    
    Use them when appropriate instead of just text responses.`;
  
  // Process the response and render components as needed
  const ui = await processAIResponse(response, components);
  return new StreamingTextResponse(ui);
}

Best Practices for Building AI Copilots

Building AI copilots that users love requires more than just implementing the technology. You need to think carefully about user experience, performance, and reliability. Here are the key practices I've learned from building these systems.

Managing Loading and Error States

Nothing frustrates users more than not knowing what's happening. When dealing with AI responses that can take several seconds, clear feedback is essential. The Vercel AI SDK provides multiple states you can use to keep users informed:

function Chat() {
  const { 
    messages, 
    isLoading, 
    error,
    reload 
  } = useChat();
  
  if (error) {
    return (
      <div className="p-4 bg-red-50 border border-red-200 rounded-lg">
        <p className="text-red-800">Something went wrong. Please try again.</p>
        <button 
          onClick={() => reload()}
          className="mt-2 px-4 py-2 bg-red-600 text-white rounded hover:bg-red-700"
        >
          Retry
        </button>
      </div>
    );
  }
  
  // Rest of your chat UI
}

For longer operations, consider adding progress indicators or status messages. Users are much more patient when they understand what's happening behind the scenes.

Setting the Context and Prompt Engineering

The quality of your AI copilot depends heavily on how you set its context. A well-crafted system prompt can make the difference between a helpful assistant and a frustrating experience. Here's an example of an effective system prompt:

const systemPrompt = `You are a helpful assistant for an e-commerce platform.
Your role is to:
- Help users find products they're looking for
- Answer questions about orders and shipping
- Provide product recommendations based on their needs
- Assist with account and payment issues

Important guidelines:
- Be concise but friendly
- Ask clarifying questions when needed
- Always verify order numbers before providing information
- If you don't know something, admit it and offer to help find the answer
- Never make up product information or prices`;

The key is being specific about the copilot's role and boundaries. Vague instructions lead to inconsistent responses. Clear guidelines ensure your copilot stays helpful and on-brand.

Handling Cost and Performance

AI API calls can get expensive quickly, especially with longer conversations. Here are strategies to manage costs while maintaining quality:

Choose the right model for the task. Not every query needs GPT-4. Use smaller, faster models for simple questions and reserve powerful models for complex tasks:

const model = isComplexQuery(message) ? 'gpt-4' : 'gpt-3.5-turbo';

Implement conversation limits. Set reasonable boundaries on conversation length:

const MAX_MESSAGES = 20;
if (messages.length > MAX_MESSAGES) {
  // Summarize and start a new conversation
}

Cache common responses. If users frequently ask the same questions, cache the responses:

const cachedResponse = await redis.get(`response:${queryHash}`);
if (cachedResponse) {
  return new Response(cachedResponse);
}

Monitor usage closely. Set up alerts for unusual spikes in API usage. A runaway loop or abuse can quickly drain your budget.

Remember, users value quick, helpful responses over perfect ones. Sometimes a faster, simpler model provides a better experience than waiting for a more sophisticated response.

Conclusion

Building AI copilots in React has never been more accessible. The Vercel AI SDK removes the complexity of streaming, state management, and UI rendering, letting you focus on creating amazing user experiences. Whether you're adding a simple chat interface or building complex generative UI systems, the tools are now available to every developer.

The key is to start simple. Build a basic chat interface first. Get comfortable with the useChat hook and streaming responses. Then gradually add more sophisticated features like generative UI and custom components. Your users will appreciate even the simplest AI features if they're well-implemented.

Remember that AI copilots aren't just about the technology. They're about creating interfaces that understand and adapt to user needs. Focus on solving real problems, provide clear feedback, and always keep the user experience at the center of your design decisions.

The future of user interfaces is conversational, adaptive, and intelligent. With the Vercel AI SDK and the practices outlined in this guide, you're ready to build that future today.

References

Vercel AI SDK Documentation

Introducing the Vercel AI SDK 3.0 with Generative UI

Build an AI-powered chat app with Next.js and the Vercel AI SDK

GitHub - vercel/ai: Build AI-powered applications with React, Svelte, and Vue

Like this project

Posted Jun 19, 2025

Transform your user experience. Learn to build powerful, chat-powered AI copilots in your React and Next.js apps using the free and easy-to-use Vercel AI SDK.

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AI Copilots in React: Building Chat-Powered UIs with Vercel's Free SDK

AI Copilots in React: Building Chat-Powered UIs with Vercel's Free SDK

The Rise of Conversational UI: Why Now?

Moving Beyond Traditional Forms and Buttons

Use Cases for AI Copilots

Introducing the Vercel AI SDK: Your Toolkit for Building with AI

What is the Vercel AI SDK?

Key Features and Benefits

Why 'Streaming-First' Matters

Building Your First Chatbot: A Step-by-Step Guide

Setting Up the Backend: The API Route

Creating the Frontend: The useChat Hook

Rendering the UI

Beyond Text: Generative UI with the Vercel AI SDK

What is Generative UI?

Example: A Dynamic Stock Ticker Component

How it Works: The render Function

Best Practices for Building AI Copilots

Managing Loading and Error States

Setting the Context and Prompt Engineering

Handling Cost and Performance

Conclusion

References

Join 50k+ companies and 1M+ independents

Creating the Frontend: The `useChat` Hook

How it Works: The `render` Function