This practical course focuses on building modern Retrieval Augmented Generation (RAG) chatbots using LangChain and popular vector databases such as FAISS and ChromaDB. Designed for developers and AI practitioners, the course demonstrates how to create production-ready RAG systems quickly while applying advanced retrieval techniques.

Learners start by building a complete RAG chatbot in minutes using free APIs and Google Colab, making the course highly accessible. The course then addresses one of the biggest challenges in RAG systems: hallucinations. Through corrective and self-correcting RAG techniques, learners explore how AI systems can verify and refine their own responses to improve reliability.

Advanced retrieval strategies are covered in depth, including reranking models, hybrid search using BM25 and vector search, HyDE retrieval, query expansion, and multi-index querying. These techniques significantly improve search accuracy and answer relevance across different knowledge sources.

The course also introduces adaptive and self-improving RAG systems that dynamically rewrite queries and learn from feedback. By the end of the course, learners will understand how to design scalable, accurate, and intelligent RAG pipelines suitable for real-world applications such as AI assistants, enterprise search, and knowledge-based chatbots.