How do I add an LLM API to my app?

Adding an LLM API to your app usually looks like this:

1) Pick a provider

Common options:

• OpenAI
• Anthropic
• Google Gemini
• Azure OpenAI
• AWS Bedrock

Compare on:

• model quality
• price
• latency
• rate limits
• tool/function calling support
• data/privacy requirements

2) Get API access

• Create an account
• Generate an API key
• Store it securely in environment variables, not in your frontend code

Example:

export LLM_API_KEY="your_key_here"

3) Decide where the call happens

Usually:

• Frontend: user interacts with UI
• Backend: app sends prompt to your server
• LLM API: your server calls the model

Best practice: call the LLM from your backend, not directly from the client, so your API key stays secret.

4) Send a request

Typical request includes:

• system instruction
• user message
• optional conversation history
• optional tools/functions
• generation settings like temperature

Example: Node.js backend

import express from "express";

const app = express();
app.use(express.json());

app.post("/chat", async (req, res) => {
  const userMessage = req.body.message;

  const response = await fetch("https://api.openai.com/v1/responses", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": `Bearer ${process.env.LLM_API_KEY}`,
    },
    body: JSON.stringify({
      model: "gpt-4.1-mini",
      input: [
        { role: "system", content: "You are a helpful assistant." },
        { role: "user", content: userMessage }
      ]
    }),
  });

  const data = await response.json();
  res.json(data);
});

app.listen(3000, () => console.log("Server running on port 3000"));

5) Parse the response

Extract the text and show it in your app. Response format depends on the provider.

6) Add safety and reliability

You’ll usually want:

• input validation
• rate limiting
• retries with backoff
• timeout handling
• logging
• moderation/content filtering if needed
• token/cost limits

7) Stream responses for better UX

Instead of waiting for the whole answer, stream tokens to the UI for a ChatGPT-like experience.

8) Manage conversation state

If your app is chat-based:

• store message history in your database
• send only relevant history to the model
• summarize old messages if the conversation gets long

9) Test and monitor

Track:

• latency
• error rates
• cost per request
• user satisfaction
• prompt quality

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Minimal architecture

User UI → Your backend → LLM API → Your backend → UI

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If you want a quick start

Tell me:

1. your stack (React, Next.js, Python, etc.)
2. which LLM provider you want
3. whether you need chat, summarization, search, or tool use

…and I can give you exact code for your app.