RAG AI acronym

RAG AI acronym stands for Retrieval-Augmented Generation, a foundational technique in artificial intelligence that enhances large language models by combining information retrieval with text generation capabilities. RAG systems retrieve relevant information from external knowledge bases, vector databases, or document collections before generating responses, significantly reducing hallucinations and improving factual accuracy. The process involves three core components: embedding user queries into vector representations, searching through indexed knowledge repositories using semantic similarity, and augmenting the language model’s context with retrieved information to generate grounded responses. RAG implementations typically utilize vector databases like Pinecone, Weaviate, or Chroma for efficient similarity search, alongside embedding models that convert text into numerical representations. This architecture enables AI systems to access up-to-date information beyond their training data cutoffs, making RAG essential for agentic AI applications requiring current knowledge, domain-specific expertise, or integration with proprietary company data. Advanced RAG techniques include multi-hop retrieval, re-ranking mechanisms, and hybrid search combining semantic and keyword approaches.