Understanding RAG (Retrieval-Augmented Generation)

RAG (Retrieval-Augmented Generation) solves the problem of AI models providing inaccurate answers about company-specific information by connecting them to external knowledge sources like internal documentation and databases. Instead of relying solely on training data, RAG systems retrieve relevant documents based on user queries, then augment the AI's response with this context to generate accurate, grounded answers. This eliminates the limitations of generic AI responses and allows organizations to build AI assistants that accurately reference their actual policies, procedures, and documentation.