RAG (Retrieval-Augmented Generation) is a paradigm that combines the strengths of large language models with retrieval systems to address challenges like static knowledge and inaccuracies. By integrating retrieval into generation, RAG systems deliver more accurate and context-aware outputs, making them effective for applications requiring current or specialized knowledge. The architecture of RAG consists of three stages: indexing, retrieval, and generation. Indexing involves encoding documents into vector representations, retrieval uses approximate nearest neighbor search algorithms to identify relevant chunks efficiently, and generation combines retrieved content with the query using structured prompts to guide the language model's response. Advanced RAG systems have evolved through various paradigms, including Naive RAG, Advanced RAG, and Modular RAG, each addressing specific challenges while building on earlier advancements. Modular RAG introduces a flexible framework designed to handle a wide range of tasks and contexts, employing specialized modules that can be dynamically reconfigured based on the requirements of the query. The implementation of RAG includes technical components such as document processing and embedding, retrieval and generation, evaluation frameworks, and quality metrics like context relevance, answer faithfulness, efficiency and latency, scalability, and specialized benchmarks like Retrieval Generation Benchmark (RGB), RECALL, and CRUD. Vector databases play a crucial role in the operation of RAG systems, providing infrastructure for storing and retrieving high-dimensional embeddings of contextual information needed for LLMs. Future developments in areas like dynamic retrieval, feedback-driven refinement, and cross-lingual capabilities will enhance their functionality, making RAG systems increasingly practical for various industries.