Tutorial June 28, 2026 12 min read

Building Your First AI Chatbot: A Complete Beginner's Guide

Sarah Lee Developer Advocate

Building an AI chatbot might sound intimidating, but with the right tools and guidance, you can have a working prototype up and running in under an hour. This guide will walk you through everything from setting up your environment to deploying a production-ready chatbot.

What We're Building

By the end of this tutorial, you'll have a fully functional AI chatbot that can:

Understand and respond to natural language queries
Maintain conversation context across multiple exchanges
Handle errors gracefully with user-friendly messages
Run entirely in the browser with a beautiful UI

Prerequisites

Before we dive in, make sure you have:

Basic knowledge of HTML, CSS, and JavaScript
A Mollyra AI account (free tier is perfect for learning)
Node.js installed on your machine (optional, for local development)
A code editor like VS Code

Getting Your API Key

Sign up at mollyra.com and navigate to your dashboard. Click "Create API Key", give it a name like "chatbot-dev", and copy the key. Keep it safe - never expose it in client-side code!

Step 1: Set Up the Project Structure

Create a new folder for your project and set up a basic HTML file with the chatbot interface:

index.html

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>AI Chatbot</title>
    <link rel="stylesheet" href="styles.css">
</head>
<body>
    <div class="chat-container">
        <div class="chat-header">
            <h3>AI Assistant</h3>
        </div>
        <div class="chat-messages" id="messages"></div>
        <form class="chat-input" id="chatForm">
            <input type="text" id="userInput" placeholder="Type your message..." required>
            <button type="submit">Send</button>
        </form>
    </div>
    <script src="chatbot.js"></script>
</body>
</html>

Step 2: Create the Chat Interface

Now let's style our chatbot to look professional and modern:

styles.css

.chat-container {
    max-width: 600px;
    margin: 2rem auto;
    background: var(--bg-card);
    border-radius: var(--radius-lg);
    border: 1px solid var(--border-default);
    overflow: hidden;
}

.chat-header {
    padding: 1rem 1.5rem;
    background: var(--bg-dark-secondary);
    border-bottom: 1px solid var(--border-default);
}

.chat-messages {
    height: 400px;
    overflow-y: auto;
    padding: 1.5rem;
}

.message {
    margin-bottom: 1rem;
    padding: 1rem;
    border-radius: var(--radius-md);
}

.message.user {
    background: var(--purple-primary);
    margin-left: 20%;
}

.message.assistant {
    background: var(--bg-dark-secondary);
    margin-right: 20%;
}

Step 3: Implement the Chat Logic

The core of our chatbot is the JavaScript that handles the conversation:

chatbot.js

const API_KEY = 'YOUR_MOLLYRA_API_KEY';
const API_URL = 'https://api.mollyra.ai/v1/chat/completions';

const chatForm = document.getElementById('chatForm');
const messagesContainer = document.getElementById('messages');
const userInput = document.getElementById('userInput');

let conversationHistory = [
    {
        role: 'system',
        content: 'You are a helpful AI assistant. Keep responses concise but informative.'
    }
];

chatForm.addEventListener('submit', async (e) => {
    e.preventDefault();
    const userMessage = userInput.value.trim();
    if (!userMessage) return;

    // Add user message to UI
    addMessage(userMessage, 'user');
    userInput.value = '';

    // Add to history
    conversationHistory.push({
        role: 'user',
        content: userMessage
    });

    // Get AI response
    try {
        const response = await fetch(API_URL, {
            method: 'POST',
            headers: {
                'Content-Type': 'application/json',
                'Authorization': `Bearer ${API_KEY}`
            },
            body: JSON.stringify({
                model: 'mollyra-x',
                messages: conversationHistory,
                stream: false
            })
        });

        const data = await response.json();
        const assistantMessage = data.choices[0].message.content;

        conversationHistory.push({
            role: 'assistant',
            content: assistantMessage
        });

        addMessage(assistantMessage, 'assistant');
    } catch (error) {
        addMessage('Sorry, something went wrong. Please try again.', 'assistant');
    }
});

function addMessage(text, sender) {
    const messageDiv = document.createElement('div');
    messageDiv.className = `message ${sender}`;
    messageDiv.textContent = text;
    messagesContainer.appendChild(messageDiv);
    messagesContainer.scrollTop = messagesContainer.scrollHeight;
}

Security Note

For production applications, never expose your API key in client-side JavaScript. Always call the API from a backend server where you can securely store your credentials.

Step 4: Add Advanced Features

Once you have the basics working, consider adding these enhancements:

Streaming Responses

Instead of waiting for the complete response, stream tokens as they arrive for a more dynamic experience:

streaming.js

const response = await fetch(API_URL, {
    method: 'POST',
    headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${API_KEY}`
    },
    body: JSON.stringify({
        model: 'mollyra-x',
        messages: conversationHistory,
        stream: true
    })
});

const reader = response.body.getReader();
const decoder = new TextDecoder();
let assistantMessage = '';

while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    const lines = chunk.split('\n');

    for (const line of lines) {
        if (line.startsWith('data: ')) {
            const data = JSON.parse(line.slice(6));
            if (data.choices[0].delta.content) {
                assistantMessage += data.choices[0].delta.content;
                // Update UI with streaming content
            }
        }
    }
}

Conversation Context Management

To prevent your conversation history from growing infinitely, implement context management:

context-management.js

const MAX_CONTEXT_MESSAGES = 20;

function manageContext() {
    if (conversationHistory.length > MAX_CONTEXT_MESSAGES + 1) {
        // Keep system message and last N messages
        const systemMessage = conversationHistory[0];
        const recentMessages = conversationHistory.slice(-MAX_CONTEXT_MESSAGES);
        conversationHistory = [systemMessage, ...recentMessages];
    }
}

Step 5: Deploy Your Chatbot

Ready to share your creation? Here are some easy deployment options:

GitHub Pages: Free hosting for static sites
Vercel: Fast deployment with serverless functions
Netlify: Drag-and-drop deployment
Your own server: Full control over the infrastructure

Next Steps

Congratulations! You've built your first AI chatbot. But this is just the beginning. Here are some ideas for what to build next:

Add file upload support for document analysis
Implement multi-language support
Build a customer support chatbot with your knowledge base
Create a code assistant that understands your codebase

Want to Build Something Amazing?

Join thousands of developers building AI-powered applications with Mollyra AI. Get started with our free tier today.

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