Building AI Tools in 2025: A Case Study with Next.js

The “Age of AI Wrappers” is in full swing. By 2025, the bar for creating intelligent software has plummeted. What once required a team of PhDs in Machine Learning can now be done by a single Fullstack developer with an API key from OpenAI or Anthropic in a weekend.

But how to transform a simple API call into a robust product, with good UX and scalable architecture?

In this article, I'm going to dissect the architecture behind the "Creator Tools" section that we recently implemented. Let's talk about prompt engineering, UI streaming, client-side PDF generation, and how Next.js is the definitive platform for this type of application.

The Anatomy of a Modern AI Application

Most AI tools follow a simple but powerful architectural pattern:

Frontend (Next.js) accepts rich user input.
API Layer (Server Actions/Route Handlers) processes these inputs and builds a "context".
LLM (OpenAI/Claude) receives the context and returns text, JSON or even UI components.
Response UI renders the result incrementally (streaming).

Let's take our Avatar Prompt Generator and ATS Analyzer as an example.

Step 1: The Power of Context (Prompt Engineering)

The secret to a good AI tool is not the model, but the prompt. In our Cover Letter Generator, we don't just send "Write me a letter". We build a structured prompt on the server:

const prompt = `
Atue como um especialista em carreira e recrutamento técnico senior.
Escreva uma carta de apresentação para o candidato ${userData.name}.

CONTEXTO DO CANDIDATO:
${userData.summary}
Habilidades: ${userData.skills.join(', ')}

VAGA ALVO:
${jobDescription}

REGRAS DE OURO:
1. Use um tom confiante, mas humilde.
2. Destaque projetos reais mencionados no resumo.
3. Seja conciso (max 3 parágrafos).
`;

This structure of Persona + Context + Task + Constraints guarantees consistently superior results.

Step 2: Streaming for Instant UX

Nobody likes waiting 10 seconds watching a spinner spin while the GPT thinks. The response must be immediate.

With the Vercel AI SDK, implementing streaming in Next.js is trivial. Instead of waiting for the full response, we transmit chunks of text as they arrive. The sensation of speed is instantaneous.

// Exemplo conceitual usando useCompletion
'use client'
import { useCompletion } from 'ai/react';

export default function PromptGenerator() {
  const { completion, input, handleInputChange, handleSubmit } = useCompletion({
    api: '/api/generate-prompt',
  });

  return (
    <div>
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
      </form>
      <div className="output">
        {completion} {/* O texto aparece caractere por caractere! */}
      </div>
    </div>
  );
}

Case Study: The Resume Generator (Reactive PDF)

One of the most complex tools we built was Resume Builder. Here, the challenge was not AI, but document manipulation.

We use the @react-pdf/renderer library to create PDFs using familiar React components (<View>, <Text>, <StyleSheet>).

The Hydration Problem

Generating PDFs on the client can be cumbersome, and if done on the server, we lose the interactivity of the "live preview". The solution was to render the PDF component on the client, but use dynamic imports so as not to block the initial main thread.

const PDFViewer = dynamic(
    () => import("@react-pdf/renderer").then((mod) => mod.PDFViewer),
    { ssr: false }
);

This allows the user to edit their data in a form on the left and see the PDF being reconstructed in real time on the right. It's a "magical" experience that traditional word processing tools can't easily replicate on the web.

ATS Analyzer: Simple vs. Simple Algorithms AI

For the ATS Analyzer (Applicant Tracking System) tool, we made an interesting engineering decision. Could we use AI to analyze the CV? Yes. But it would be expensive and slow.

Instead, we use classic text processing (lite Natural Language Processing) on the client:

We remove "stop words" (a, o, de, para).
We tokenize the job description into keywords.
We compare with the CV tokens.

The result is instantaneous (0ms latency), runs offline and costs zero. Sometimes the best "AI" is a good regex algorithm.

Lesson: Don't use AI cannons to swat flies of simple logic.

Monetization and the Future of Micro-SaaS

Tools like these are perfect examples of Micro-SaaS. They solve specific problems (making a CV, writing a letter, generating an avatar) and have high perceived value.

With Next.js, Stripe, and an AI API, a developer can launch a global product in weeks. The operational cost is minimal (Serverless), scaling to zero when there are no users.

Conclusion

Building AI tools with Next.js is not just about integrating APIs; it’s about orchestrating user experience, performance and real value.

We are living in the "Personal Software Renaissance." Tools that were once the domain of large corporations can now be built, deployed, and scaled by independent creators.

The code for these tools is available in this project. Explore the /src/components/creator directory, study the patterns, and most importantly, build something of your own.

What AI tool would you like to see built? Leave your suggestion!