RAG Architecture for Enterprise Knowledge Systems

The RAG Architecture Stack

A production RAG system is not a single component but a pipeline of specialized modules: a document ingestion and preprocessing layer, a chunking and embedding engine, a vector store for semantic search, a reranking stage for precision, and a generation layer that synthesizes context into coherent responses with citations.

• Document ingestion: multi-format parsing, OCR, normalization
• Chunking: semantic chunking vs. fixed-size vs. hierarchical
• Embedding: OpenAI ada-002, Cohere, custom domain embeddings
• Vector stores: Pinecone, Weaviate, pgvector
• Reranking: Cohere Rerank, cross-encoder models

Chunking Strategies

Chunking strategy has an outsized impact on retrieval quality. Fixed-size chunking is simple but breaks semantic coherence. We use a hybrid approach: semantic chunking for prose documents, structural chunking for tables and forms, and hierarchical chunking for long documents where both section-level and paragraph-level context matter.

Hybrid Search

Pure vector search misses keyword-exact matches that are critical in enterprise contexts (product codes, names, regulations). We implement hybrid search combining dense vector retrieval with sparse BM25 retrieval, using Reciprocal Rank Fusion (RRF) to merge results. This typically improves recall@10 by 15–25% over pure vector search.

Citation and Grounding

In enterprise settings, every RAG response must be grounded in source documents with precise citations. We implement document-level and passage-level attribution, presenting sources alongside generated answers. This enables end users to verify claims and builds trust in the system.

Deployment at Scale

Production RAG deployments must handle concurrent requests, maintain low latency under load, and support continuous document updates. We architect for horizontal scalability using async embedding pipelines, distributed vector stores, and response caching for frequently accessed queries.