Building a Retrieval-Augmented Generation (RAG) System for Domain-Specific Document Querying
In recent years, Retrieval-Augmented Generation (RAG) has emerged as a powerful method for enhancing large language models with structured access to external document collections. By combining dense semantic search with contextual text generation, RAG systems have proven particularly useful for tasks such as answering questions based on extensive documentation, enabling
