Retrieval-Augmented Generation (RAG) is one of the most effective ways to bring corporate data together with AI.
How Does RAG Work?
The RAG system takes a user query, finds the most relevant documents in a vector database, and feeds this context to an LLM to generate accurate answers. It significantly reduces hallucination rates.
Enterprise Use Cases
In areas like HR policies, technical documentation, customer FAQs, and legal contracts, RAG systems reduce employees' information access time by 70%.
Technology Choices
For vector databases: Pinecone, Weaviate, or Qdrant. For embedding models: OpenAI text-embedding-3 or Cohere embed v3.