🪞 Introduction
Let’s be honest: AI is everywhere right now.
Every other product demo seems to have a “magic ✨” button that generates something… but too often, it’s a solution in search of a problem.
Over the past year, I’ve experimented with integrating generative AI into real production apps. And while it’s incredibly powerful, I’ve also seen how easy it is to overdo it. Just because you can add an AI feature doesn’t mean you should.
In this post, I’ll walk through how to integrate generative AI into a React app thoughtfully. In a way that adds genuine value, not noise. We’ll talk architecture, UX, code, and the mindset shift that separates “cool demos” from useful products.
🧠The Problem with “AI for AI’s Sake”
We’ve all seen it:
“Now with AI!” … but what does that even mean?
A to-do app that lets you “chat” with your tasks. ??
A note-taking app that “summarizes” a single line of text. ??
An ecommerce site that “describes” a product it already listed. ??
These are gimmicks, and the problem isn’t the technology; it’s the intent. Somtimes businesses and/or teams add AI just to check a box, instead of solving a real user pain point.
My personal rule of thumb:
If the feature doesn’t measurably improve the user experience, it’s not worth building.
⚙️ Identifying Real Use Cases
Before wiring up the OpenAI API, it’s worth asking: Where would AI actually help?
Here are three scenarios where it usually makes sense:
- Automating repetitive work – summarizing content, writing drafts, reformatting data.
- Improving personalization – adapting tone, recommendations, or layout to user intent.
- Understanding natural input – allowing users to type what they mean instead of forcing rigid inputs.
Practical examples for React apps:
- A blog editor that helps rewrite paragraphs or generate summaries.
- A documentation site that offers conversational search.
- A dashboard that translates natural-language queries (“show me revenue from Q2”) into structured filters.
These are cases where AI enhances productivity or usability, not distracts from it.
🧩 Architecture Overview
A clean AI integration doesn’t require a rewrite. Here’s a simple, scalable setup that works for most use cases:
React frontend → /api/ai/* (Node/Next API routes) → LLM provider (OpenAI, Anthropic, etc.)The key is separation of concerns:
- Keep LLM logic server-side.
- Keep UI and state management client-side.
- Use async hooks like useQuery, useMutation, or useAsync to fetch results.
Example (simplified with React Query(TanStack Query)… I love React Query, BTW!):
const { data, isLoading } = useQuery(['ai-summary', postId], () =>
fetch(`/api/summary?post=${postId}`).then(res => res.json())
);This approach makes your AI feature behave like any other asynchronous data source. It’s predictable, testable, and maintainable.
🧑‍💻 Example: Building an AI-Powered Summary Tool
Let’s build something small but genuinely useful: A Text (or Post) Summarizer.
Users paste an article, hit “Summarize,” and the app returns a concise, readable overview.
Frontend (React/Next.js):
// components/SummaryForm.tsx
import { useState } from "react";
export default function SummaryForm() {
const [text, setText] = useState("");
const [summary, setSummary] = useState("");
const [loading, setLoading] = useState(false);
async function handleSubmit(e) {
e.preventDefault();
setLoading(true);
const res = await fetch("/api/summary", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ text }),
});
const data = await res.json();
setSummary(data.summary);
setLoading(false);
}
return (
<form onSubmit={handleSubmit} className="summary-form">
<textarea
value={text}
onChange={(e) => setText(e.target.value)}
placeholder="Paste your text here..."
/>
<button type="submit" disabled={!text || loading}>
{loading ? "Summarizing..." : "Summarize"}
</button>
{summary && <div className="summary-result">{summary}</div>}
</form>
);
}Backend (Next.js API route):
// pages/api/summary.ts
import OpenAI from "openai";
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
export default async function handler(req, res) {
const { text } = req.body;
const completion = await openai.chat.completions.create({
model: "gpt-4-turbo",
messages: [
{ role: "system", content: "You are a concise summarizer." },
{ role: "user", content: `Summarize this:\n${text}` },
],
});
res.json({ summary: completion.choices[0].message.content });
}It’s lightweight, useful, and demonstrates how AI can fit naturally into an existing React app without turning it into a “prompt playground.”
🎨 Designing for Trust and Clarity
The UI layer matters… a lot.
If your AI feature looks like a black box, users won’t trust it.
A few simple rules:
- Be transparent: Label generated content (“AI-assisted summary”).
- Give users control: Allow edits or regenerations.
- Provide feedback: Use spinners, progress bars, and readable errors.
Good AI UX is about confidence. The user should always feel in charge, not outsmarted.
When I worked on Jetpack AI’s frontend flows, that was a key focus: clear affordances, predictable results, and strong human control loops. Those lessons apply anywhere you build with AI.
🔍 Common Pitfalls to Avoid
A few mistakes I’ve made (and learned from):
- Sending too much data – you’ll hit token limits and slow everything down.
- Relying entirely on AI output – always validate or sanitize responses.
- Ignoring cost implications – keep your LLM calls lean.
- Skipping error handling – rate limits and timeouts will happen.
The best AI features feel boring in the best way: they work reliably, integrate smoothly, and solve real problems.
đź’ˇ Conclusion
AI isn’t the star of the show. Your user is!
The goal isn’t to make your React app look smarter, it’s to make it feel more helpful.
When integrated thoughtfully, generative AI can be a genuine force multiplier for your users, just as it can be for developers too.
If you’re building or experimenting with similar ideas, I’d love to hear about it! Feel free to share in the comments or tag me on X/Twitter or LinkedIn.
🧾 Resources
- OpenAI JavaScript SDK
- Anthropic API Docs
- React Query
- Jetpack AI Product Page (an example of AI being thoughtfully integrated into Gutenberg, enhancing the content creation experience)