Retrieval-Augmented Generation — an architecture where a language model answers from your data by retrieving relevant context from a vector database before generating.
A RAG (Retrieval-Augmented Generation) system combines a language model with a retrieval layer over your private data. When a user asks a question, the system first retrieves the most relevant chunks of context from a vector database (using semantic similarity over embeddings), then assembles those chunks into the model's prompt, then generates an answer grounded in the retrieved content. RAG is the standard architecture for "ask questions over our knowledge base" use cases.
Most enterprise AI use cases — internal knowledge bases, customer support over documentation, regulated industry Q&A — require answers grounded in private data the model was never trained on. Fine-tuning is expensive and freezes the data; RAG keeps the data live and external. The challenge is that production-grade RAG is much harder than the tutorial — chunking strategy, embedding model selection, top-k tuning, and evaluation discipline all decide whether the system actually works.
Production RAG is an engineering problem more than a model problem. Across Skopa engagements, the difference between a tutorial-grade RAG and one that holds up at scale comes down to: chunk size and overlap calibrated against an evaluation set (not the default values), embedding model selected for the domain (not just the popular one), reranking after retrieval, observability over what context the model actually used, and rigorous quality measurement on real user questions. We routinely see top-k cut from 10 to 3 with quality improvement and 70% cost reduction simultaneously.