Building Your First AI-Powered Application: A Complete Guide

Creating AI-powered applications has never been more accessible. In this comprehensive tutorial, we'll walk through building a complete AI application from scratch, covering everything from setup to deployment.

What You'll Build

We'll create a smart content analyzer that can:

• Analyze text sentiment
• Extract key topics
• Generate summaries
• Provide content recommendations

Prerequisites

Before we start, make sure you have:

• Basic knowledge of JavaScript/TypeScript
• Node.js installed on your machine
• An OpenAI API key
• A code editor (VS Code recommended)

Project Setup

1. Initialize Your Project

mkdir ai-content-analyzer
cd ai-content-analyzer
npm init -y

2. Install Dependencies

npm install next react react-dom typescript @types/react @types/node
npm install openai axios cheerio
npm install -D tailwindcss postcss autoprefixer

3. Configure Environment Variables

Create a .env.local file:

OPENAI_API_KEY=your_openai_api_key_here

Building the Core Components

API Route for AI Processing

// pages/api/analyze.ts
import { NextApiRequest, NextApiResponse } from 'next';
import { OpenAI } from 'openai';

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

export default async function handler(
  req: NextApiRequest,
  res: NextApiResponse
) {
  if (req.method !== 'POST') {
    return res.status(405).json({ error: 'Method not allowed' });
  }

  const { text, analysisType } = req.body;

  try {
    let prompt = '';
    
    switch (analysisType) {
      case 'sentiment':
        prompt = `Analyze the sentiment of this text and provide a score from -1 (very negative) to 1 (very positive): "${text}"`;
        break;
      case 'summary':
        prompt = `Provide a concise summary of this text: "${text}"`;
        break;
      case 'topics':
        prompt = `Extract the main topics from this text: "${text}"`;
        break;
      default:
        prompt = `Analyze this text: "${text}"`;
    }

    const completion = await openai.chat.completions.create({
      model: 'gpt-3.5-turbo',
      messages: [{ role: 'user', content: prompt }],
      max_tokens: 500,
    });

    const result = completion.choices[0].message.content;
    res.status(200).json({ result });
  } catch (error) {
    console.error('Error:', error);
    res.status(500).json({ error: 'Analysis failed' });
  }
}

Conclusion

Building AI-powered applications is an exciting journey that opens up countless possibilities. With the foundation you've built here, you can expand and customize your application to meet specific needs and requirements.

Happy coding, and welcome to the world of AI development!