A Simple Guide to Retrieval Augmented Generation

RAG combines retrieval of relevant information from a knowledge base with generative AI models to produce accurate, context-aware responses, making it essential for applications that require up-to-date or domain-specific knowledge. [1] [2]

Semantic chunking groups sentences by meaning using embeddings, while agentic chunking uses LLM agents to create task-specific chunks, both enhancing retrieval accuracy and contextual relevance. [1] [2]

Hybrid retrieval combines keyword and semantic search, while iterative retrieval refines queries based on initial results, both improving precision and recall over simple retrieval methods. [1]

By breaking down RAG into interchangeable modules for indexing, retrieval, and generation, modular RAG allows rapid experimentation, easier maintenance, and seamless integration of new technologies. [1]

Multimodal RAG extends standard RAG to handle text, images, audio, and video, enabling richer, context-aware applications across diverse data types. [1] [2]

Knowledge graph RAG integrates entity relationships into retrieval, supporting deep contextual reasoning and answering questions that span multiple documents. [1]

Agentic RAG uses LLM agents to make autonomous decisions in chunking, embedding selection, adaptive retrieval, and dynamic prompt engineering, enabling more intelligent and adaptive systems. [1]

Techniques like contextual prompting, controlled generation, few-shot prompting, and chain-of-thought prompting ensure LLM responses are accurate and relevant to the retrieved context. [1]

RAG systems are evaluated using retrieval and RAG-specific metrics, with benchmarks like RGB and Multi-hop RAG assessing accuracy, robustness, and information integration. [1] [2]

RAG is cost-efficient for dynamic knowledge needs, fine-tuning excels with static domain data, and hybrid approaches combine both for optimal performance in complex scenarios. [1]