Architecting Low‑Latency Vector Search for Real‑Time Retrieval‑Augmented Generation Workflows
Introduction Retrieval‑Augmented Generation (RAG) has emerged as a powerful paradigm for building LLM‑driven applications that need up‑to‑date, factual, or domain‑specific knowledge. In a RAG pipeline, a vector search engine quickly retrieves the most relevant passages from a large corpus, and those passages are then fed into a generative model (e.g., GPT‑4, Llama‑2) to produce a grounded answer. When RAG is used in real‑time scenarios—chatbots, decision‑support tools, code assistants, or autonomous agents—latency becomes a first‑order constraint. Users expect sub‑second responses, yet the pipeline must: ...