Why Production RAG Needs More Than Vector Search

Author(s): Dave R – Microsoft Azure & AI MVP☁️ Originally published on Towards AI. How hybrid search, graph retrieval, agents, and evaluation turn a basic RAG pipeline into a production architecture. My RAG prototype looked complete: ingest documents, create embeddings, store vectors, retrieve a few chunks, and send them to a language model. That flow is enough to prove the idea. It is not enough to explain what happens when the corpus grows, queries become less predictable, answers depend on several sources, or you need a way to measure whether retrieval is improving. Why Production RAG Needs More Than Vector SearchThe article argues that production RAG is largely about reliable context, not about the LLM itself: it breaks down practical retrieval patterns (context window, project/session retrieval, and RAG) and shows why document search requires an ingestion pipeline with parsing, chunking, metadata, and careful vectorization. It then explains why a single vector query isn’t enough—combining keyword + vector hybrid search (including RRF), adding graph retrieval for relationship-heavy questions, using semantic re-ranking, and moving from fixed retrieval to agentic query planning for multi-step requests. Finally, it emphasizes continuous evaluation and observability using separate metrics for retrieval quality, groundedness, and answer relevance, presenting a production architecture with offline ingestion, online retrieval/generation, and a feedback loop, plus an example implementation in .NET and Blazor where structured output and citations support UI rendering and validation. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI