How to Build an AI Chatbot for Your Business

import openai
from openai import OpenAI

client = OpenAI()

def chat_with_bot(messages, context):
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": f"You are a helpful assistant. Use this context: {context}"},
            *messages
        ]
    )
    return response.choices[0].message.content

Step 5: Add RAG for Better Answers

Connect your knowledge base using RAG:

1. Embed all documents into a vector database
2. On each query, find the most relevant documents
3. Include them in the prompt as context

Step 6: Deploy and Monitor

Deploy using Vercel, Railway, or a dedicated server. Monitor:

• Response quality and accuracy
• User satisfaction ratings
• Common failure modes

Step 7: Iterate

Use real conversations to improve your chatbot. Add new knowledge, refine prompts, and handle edge cases.

Use Case: E-commerce Support

A Moroccan e-commerce store built a chatbot that handles 80% of customer inquiries automatically — order tracking, return requests, and product recommendations. Average response time dropped from 4 hours to 30 seconds.

Next Steps

Our "Build with LLMs" programme teaches you to build production-ready chatbots and AI applications from scratch.