AI and React App

Approach

Rather than retrofitting a chatbot or plugging in something generic, we started with the business workflow and use case — not the tech.

Our goal: identify where AI could actually solve a real problem without overwhelming the stack or user.

Step 1: Identify AI-worthy Use Cases

We started with user behavior analytics and feedback. Three use cases stood out:

• Smart form assistance: guide users through long data input steps
• Auto-generated summaries of dashboard reports
• Natural language Q&A on platform features

These were pain points where automation could enhance, not distract.

Step 2: Add AI Logic Without Bloating the Frontend

We kept the frontend lean by using:

• fetch() and axios to call backend routes securely
• debounce and async/await for optimal UX timing
• Suspense boundaries for loading fallback handling

On the backend, we created middleware with Laravel (could be Node as well) that handled:

• Prompt engineering per use case
• Authentication and rate-limiting
• Context injection (user, plan, past usage)

We routed requests to OpenAI and Anthropic based on function — OpenAI for summaries, Claude for broader answers.

Step 3: Keep Control With Custom Prompts

We didn’t use base LLMs directly. Instead, we created our own prompt templates using:

• Internal terminology
• Guardrails to avoid hallucinations
• Data-specific context pulled from the app database

Each prompt was dynamically assembled server-side. This kept the agent both smart and safe.