We've recently rolled out Devin vs Copilot AI development tools in our team and accumulated a good deal of experience. Sharing it here as a reference, hoping it helps others tackling similar challenges.

Core Concepts

Building on this foundation, we can further optimize:

"use client";
import { useChat } from "ai/react";

export function AIChat() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } =
    useChat({
      api: "/api/chat",
    });
  return (
    <div className="chat-container">
      {messages.map((m) => (
        <div key={m.id} className={`message ${m.role}`}>
          <p>{m.content}</p>
        </div>
      ))}
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
        <button type="submit" disabled={isLoading}>
          发送
        </button>
      </form>
    </div>
  );
}

This pattern is very practical in large-scale projects and can significantly reduce maintenance costs.

Deep Dive

In a real project, the usage gets a bit more complex:

import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";

export async function POST(req) {
  const { messages } = await req.json();
  const result = await streamText({
    model: openai("gpt-4o"),
    messages,
    system: "你是一个专业的前端开发助手。",
    maxTokens: 2000,
  });
  return result.toDataStreamResponse();
}

This approach improves both the testability and scalability of the code.

Real-World Implementation

Here is a complete example:

"use client";
import { useChat } from "ai/react";

export function AIChat() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } =
    useChat({
      api: "/api/chat",
    });
  return (
    <div className="chat-container">
      {messages.map((m) => (
        <div key={m.id} className={`message ${m.role}`}>
          <p>{m.content}</p>
        </div>
      ))}
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
        <button type="submit" disabled={isLoading}>
          发送
        </button>
      </form>
    </div>
  );
}

Pay attention to edge-case handling—this is crucial in production environments.

Tuning Strategies

The key is to understand the core logic:

import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";

export async function POST(req) {
  const { messages } = await req.json();
  const result = await streamText({
    model: openai("gpt-4o"),
    messages,
    system: "你是一个专业的前端开发助手。",
    maxTokens: 2000,
  });
  return result.toDataStreamResponse();
}

Performance optimization must be tailored to specific scenarios—not every situation calls for aggressive optimization.

Summary

• Always verify compatibility before using in production
• In team collaboration, conventions and documentation matter more than the technology itself
• Stay up-to-date with community trends; technical solutions require continuous iteration