Enterprise RAG Architecture
Production-ready Retrieval-Augmented Generation system with ingestion pipeline, vector store, retrieval layer, and LLM response generation with citation tracking.
Architecture Diagram
┌─────────────────────────────────────────────────┐ │ ENTERPRISE RAG SYSTEM │ ├──────────────────┬──────────────────────────────┤ │ Ingestion │ Query Processing │ │ Pipeline │ │ │ │ User Query │ │ ┌───────────┐ │ │ │ │ │ Document │ │ ▼ │ │ │ Store │ │ Embedding Model │ │ └─────┬─────┘ │ │ │ │ │ │ ▼ │ │ Chunking │ Vector Search ──────────► │ │ │ │ │ Vector DB │ │ Embedding │ ▼ │ │ │ │ LLM + Context │ │ ▼ │ │ │ │ Vector DB │ ▼ │ └──────────────────┴── Response + Citations ──────┘
Key Components
Retrieval-Augmented Generation (RAG) is the backbone of most enterprise AI knowledge systems deployed today. This architecture pattern solves the fundamental limitation of LLMs — their static knowledge cutoff — by dynamically retrieving relevant context from your own data at query time. This reference architecture reflects how I design and implement RAG systems for enterprise clients.
The Two-Pipeline Design
Enterprise RAG has two distinct pipelines that must be designed independently:
- Ingestion Pipeline (offline): Document loading → chunking → embedding → vector store indexing. Runs on a schedule or event trigger.
- Query Pipeline (real-time): Query embedding → vector search → context assembly → LLM prompt → response generation. Must be sub-second.
- These pipelines share only the embedding model and vector store — keeping them decoupled simplifies debugging and scaling.
Chunking Strategy — Where Most RAG Systems Fail
Chunk size is the most impactful and most underestimated decision in RAG design. Too large, and context windows overflow and irrelevant text dilutes answers. Too small, and you lose semantic coherence.
- Fixed-size chunking (256–512 tokens): Simplest, works for uniform documents
- Semantic chunking: Split on paragraph/sentence boundaries — better recall for structured docs
- Hierarchical chunking: Small chunks for retrieval, parent chunks for context — best for long documents
- Overlap: 10–20% overlap between chunks prevents answer truncation at boundaries
Hybrid Search: Semantic + Keyword
Pure vector search fails on exact-match queries (product codes, IDs, names). Production enterprise RAG systems use hybrid search that combines semantic (vector) and keyword (BM25) signals, re-ranked with a cross-encoder.
- Semantic search: Best for conceptual queries ('What is the process for...')
- BM25 keyword search: Best for exact matches and entity lookups
- Reciprocal Rank Fusion (RRF): Merge ranked lists without score normalization issues
- Cross-encoder reranking: Final pass with a smaller model for precision improvement
Enterprise Access Control
Document-level access control is a hard requirement in regulated industries. The architecture enforces permissions at retrieval time, not just at ingestion time, to prevent document leakage as permissions change.
- Metadata filtering: Each chunk stores document ACL metadata in the vector store
- Pre-filter (preferred): Filter before ANN search — faster but requires ACL in vector payload
- Post-filter: Apply ACL after retrieval — simpler but wastes compute on unauthorized results
- Row-level security: Snowflake Cortex and some managed vector DBs support native RLS
Observability and Evaluation
A RAG system without evaluation is a system you can't trust. Define your eval metrics before go-live and track them continuously in production.
- Retrieval recall@k: Are relevant docs in the top-k retrieved chunks?
- Answer faithfulness: Is the answer grounded in retrieved context? (No hallucination)
- Answer relevance: Does the answer address the question?
- Context precision: What % of retrieved chunks actually contributed to the answer?
- Latency: Track p50/p95 for both retrieval and LLM generation separately
Design Principles
Used In
- Internal knowledge base Q&A for enterprise clients
- Policy and procedure retrieval for regulated industries
- Technical documentation search for engineering teams
- Customer support knowledge retrieval systems
Takeaway
Enterprise RAG is deceptively complex to do well. The demo with 100 documents always works. Production with 100,000 heterogeneous documents, changing ACLs, multiple document types, and latency requirements under 2 seconds requires careful design at every layer. Invest in chunking strategy, hybrid retrieval, and evaluation infrastructure before anything else.