Building RAG Chatbots for Company Knowledge Bases

Keeping internal company policies, HR guidelines, and administrative regulations accessible to employees can be a major operational challenge. This course shows you how to solve this by building an intelligent, automated Q&A chatbot that retrieves answers directly from corporate documents. You will transition from understanding basic AI concepts to designing a functional Retrieval-Augmented Generation (RAG) pipeline. Through written explanations and practical code examples, you will learn how to process text documents, store them securely in a vector database, and query them to generate accurate, context-aware answers. What you'll learn: Understand the foundational concepts of Retrieval-Augmented Generation and vector embeddings; Prepare and chunk corporate documents like HR policies and admin guidelines for AI processing; Configure a vector database to store and search company knowledge efficiently; Apply prompt engineering techniques to ensure the chatbot provides safe and accurate answers; Design conversational workflows that allow employees to self-serve information; Implement basic security and privacy practices for handling sensitive internal data. The course begins with core terminology and the architectural theory behind RAG systems before guiding you through step-by-step text-based implementation guides. You will explore practical patterns for indexing documents, managing vector searches, and refining chatbot responses. This course is designed for beginners, developers, HR tech enthusiasts, and IT administrators looking to automate internal support, with no prior experience with AI or vector databases required. Start reading today to build your first intelligent employee self-service assistant.