This intermediate course path introduces the core concepts and implementation patterns behind Retrieval-Augmented Generation using JavaScript. You will learn how RAG combines information retrieval with language generation to improve response relevance and reduce unsupported outputs. The path covers text representation methods such as Bag-of-Words and semantic embeddings, then moves into vector database workflows using document preprocessing, chunking, embedding storage, metadata filtering, and multi-chunk retrieval. You will also explore how different representation techniques affect semantic search and document retrieval performance. Later courses focus on improving RAG pipelines with hybrid retrieval, query refinement, context building, prompt construction, and output constraints that keep responses tied to retrieved sources. This path is designed for learners who have JavaScript experience and want to build a practical foundation in RAG system design.
