Retrieval-Augmented Generation (RAG) is the cutting-edge enterprise AI pattern that grounds large language models in private company documents, policies, databases, and knowledge bases. By retrieving relevant document chunks via vector similarity search before generating answers, RAG eliminates hallucinations and guarantees cited, verifiable responses.
Shish Technology architects end-to-end RAG pipelines utilizing hybrid semantic search, chunk reranking, Pinecone/Qdrant vector stores, and automated document ingestors.
