How do I add chat and text generation to my app with an LLM API?

To add chat and text generation to your app with an LLM API, the basic pattern is:

1. Pick an API provider

   • Examples: OpenAI, Anthropic, Google, Azure OpenAI, etc.
   • Choose based on model quality, price, latency, tool support, and safety features.

2. Create a backend endpoint in your app

   • Don’t call the LLM API directly from the browser/mobile app if it requires a secret API key.
   • Your app’s client sends user input to your server, and your server calls the LLM API.

3. Send a prompt or message history

   • For text generation, send a prompt like “Write a product description for…”
   • For chat, send a list of messages:
     • system: instructions/behavior
     • user: user’s message
     • assistant: previous responses

4. Return the model’s output to the UI

   • Display it in your chat window or editor.
   • Optionally use streaming so text appears token-by-token for a better UX.

5. Add guardrails

   • Validate input length.
   • Handle rate limits/errors.
   • Filter sensitive content if needed.
   • Log usage and monitor costs.

---

Typical architecture

Client app

• Chat box / prompt input
• Displays generated text

Your backend

• Receives user message
• Adds system instructions
• Calls LLM API
• Streams or returns result

LLM provider

• Generates the response

---

Minimal example flow

Chat request

Your backend sends something like:

{
  "model": "some-model",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Help me write a short email to a client."}
  ]
}

Text generation request

{
  "model": "some-model",
  "input": "Write a short email to a client apologizing for the delay."
}

---

Example implementation idea

Backend route (pseudo-code)

from flask import Flask, request, jsonify
import os

app = Flask(__name__)

@app.post("/api/chat")
def chat():
    user_message = request.json["message"]

    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": user_message}
    ]

    # Call your LLM provider here
    result = llm_client.generate(messages=messages)

    return jsonify({"reply": result})

Frontend

async function sendMessage(message) {
  const res = await fetch("/api/chat", {
    method: "POST",
    headers: {"Content-Type": "application/json"},
    body: JSON.stringify({ message })
  });

  const data = await res.json();
  displayReply(data.reply);
}

---

Best practices

• Use a system prompt to define tone and rules.
• Keep conversation history if you want the model to remember context.
• Stream responses for chat-like experiences.
• Trim old messages to stay within token limits.
• Cache or summarize long conversations for cost control.
• Never expose your API key in the frontend.

---

Features you may want next

• Function calling / tool use: let the model trigger actions like search, database lookup, or sending emails.
• RAG (Retrieval-Augmented Generation): connect the model to your documents.
• Moderation/safety filters
• Multi-turn memory
• Prompt templates for reusable tasks

If you want, I can show you a working example in Node.js, Python, or React using a specific LLM API.