🧠 How to Build an LLM-Powered App in Next.js (2025 Guide)
✨ Introduction
Large Language Models (LLMs) like OpenAI's GPT-4 and Mistral are transforming how we build apps. From chatbots to content generators, integrating LLMs into your Next.js project is easier than ever in 2025.
🛠️ Why Use Next.js for LLM Apps?
- Supports serverless and edge functions
- Full-stack flexibility (API + frontend)
- Built-in SSR, SSG, and streaming
- Great DX with App Router and RSC
📦 Prerequisites
- Node.js 18+
- Next.js app:
npx create-next-app@latest - OpenAI API key
- Basic React knowledge
🧱 Folder Structure Example
/app
/chat
page.tsx → Chat UI
/api
/chat
route.ts → LLM API handler🔌 Setting Up the LLM API Route
// /app/api/chat/route.ts
import { NextResponse } from 'next/server'
export async function POST(req: Request) {
const { messages } = await req.json()
const response = await fetch('https://api.openai.com/v1/chat/completions', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
Authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
},
body: JSON.stringify({
model: 'gpt-4',
messages,
stream: false,
}),
})
const data = await response.json()
return NextResponse.json({ reply: data.choices[0].message.content })
}
💡 Tip: For best performance, set runtime: 'edge'
💬 Building the Frontend Chat UI
'use client'
import { useState } from 'react'
export default function ChatPage() {
const [messages, setMessages] = useState([{ role: 'user', content: '' }])
const [input, setInput] = useState('')
const sendMessage = async () => {
const updatedMessages = [...messages, { role: 'user', content: input }]
setMessages(updatedMessages)
setInput('')
const res = await fetch('/api/chat', {
method: 'POST',
body: JSON.stringify({ messages: updatedMessages }),
})
const data = await res.json()
setMessages([...updatedMessages, { role: 'assistant', content: data.reply }])
}
return (
<div className="p-4 max-w-xl mx-auto">
{messages.map((m, i) => (
<p key={i}><strong>{m.role}:</strong> {m.content}</p>
))}
<input
className="border p-2 w-full mt-4"
value={input}
onChange={(e) => setInput(e.target.value)}
onKeyDown={(e) => e.key === 'Enter' && sendMessage()}
placeholder="Type a message..."
/>
</div>
)
}🚀 Going Further
- Streaming with ReadableStreams
- Use Pinecone or Weaviate for RAG
- Add auth via NextAuth.js
- Store chats in PostgreSQL or MongoDB
- Deploy with Vercel
🔐 Example .env
OPENAI_API_KEY=sk-...🌎 Deploying to Production
Run the following command to deploy:
vercelSet your environment variables in the Vercel dashboard first.
🧠 Final Thoughts
With LLMs and Next.js, you're not just building apps — you're crafting intelligent tools that think, assist, and create.
Build the future with code. 🚀
