How RAG for customer support improves accuracy at scale

This post details how Retrieval-Augmented Generation (RAG) can be applied to customer support to improve accuracy and scalability. It explains the RAG pipeline stages (ingestion, indexing, retrieval, augmentation, generation) with a focus on how support data is indexed into vector embeddings for semantic search and how retrieval selects relevant context. It also discusses generation, LLM quality, integration flexibility, cost, and security considerations for choosing an AI system for customer support. The post provides criteria for determining company readiness for RAG, including data maturity, ticket volume, automation goals, and technical capacity. It differentiates RAG from fine-tuning and introduces agentic RAG as a more advanced approach.