Retrieval-Augmented Generation (RAG) has become the backbone of many production LLM applications, yet the internal workflow is often treated as a black box. This signal walks through what happens from the moment a user submits a query: input normalization, embedding generation, vector database search, optional reranking, context assembly, and finally the LLM generation step. Each stage introduces latency and quality trade-offs, and understanding them is critical for optimizing response times and answer accuracy. For engineers building RAG systems, key considerations include choosing the right embedding model, tuning top-k retrieval parameters, and designing effective prompt templates that incorporate retrieved context without overwhelming the model. The post also touches on common pitfalls like retrieval noise and context window limits. This is evergreen knowledge for AI developers, as RAG remains a core pattern for grounding LLMs with external data.
A practical breakdown of the RAG online workflow, covering query processing, retrieval, reranking, and generation stages, with engineering insights for building production-grade LLM applications.