Retrieval-Augmented Generation (RAG) is an AI technique that enhances large language models like GPT by integrating information retrieval with text generation, enabling models to produce contextually richer and more accurate responses by accessing current and specific data. This tutorial outlines how to implement a Python RAG chatbot using OpenAI's GPT models in conjunction with Search Engine Results Page (SERP) data. Leveraging Bright Data’s SERP API, users can scrape search engine data from Google and other platforms, overcoming anti-bot challenges and using this data as a context for AI requests. The tutorial provides step-by-step instructions for setting up a Python environment, configuring necessary libraries, and constructing a Streamlit-based UI for user interaction, allowing users to input search queries and prompts to receive detailed AI-generated responses. Despite the complexity and cost of scraping search engines, this approach demonstrates how RAG can be effectively utilized to improve the accuracy of AI outputs by employing real-time data retrieval.