Building an RAG Chatbot Using Python, LangChain, FAISS & OpenAI

Prerequisites

Basic coding knowledge in any language e.g. JavaScript, PHP, Python, Java etc.

Overview

In this era of Large Language Models (LLMs), ensuring factual accuracy and contextual relevance in AI-generated responses is a critical challenge. Retrieval-Augmented Generation (RAG) bridges this gap by enhancing LLMs with real-time, domain-specific information retrieval. This project provides a hands-on experience in building a fully functional RAG-powered chatbot, covering the complete pipeline—from data ingestion and vector search to retrieval and response generation. By the end of the program, you will have gained practical experience with embeddings, vector search, retrieval optimization, tools, chains, agents, LangChain, FAISS, and OpenAI.

Problem                                 

Traditional AI chatbots rely solely on pre-trained LLMs, often generating hallucinated or outdated responses. These models struggle with:

• Lack of contextual grounding – Responses are based only on pre-trained knowledge.
• No real-time retrieval – They can't fetch updated or domain-specific information.
• Inability to customize knowledge – Businesses need a way to integrate proprietary data dynamically.

This leads to unreliable AI assistants that cannot be trusted for business-critical use cases.

Solution

This project introduces a Retrieval-Augmented Generation (RAG) chatbot built using Python, LangChain, FAISS, and OpenAI. Instead of relying solely on an LLM’s pre-trained knowledge, the chatbot will dynamically retrieve relevant information from an indexed document store before generating responses.

Project Outline

Trainer

Brian Cheboi

Software Engineer – SaaS, AI, E-commerce

LinkedIn: https://www.linkedin.com/in/brian-cheboi/

About the Trainer

Brian Cheboi is a software engineer specializing in AI-powered SaaS, e-commerce, and big data solutions. With experience in finance, real estate, and enterprise AI, he has developed platforms that drive business automation, intelligent decision-making, and large-scale efficiency. Brian recently built an MVP that secured $1M in funding from a Dubai-based accelerator. He is passionate about leveraging technology to create impactful solutions that drive sustainable business growth.