Enterprise RAG Systems
Production-grade Retrieval-Augmented Generation architecture for enterprise knowledge systems.
Retrieval-Augmented Generation (RAG) is the dominant architecture for enterprise AI systems that require factual grounding, domain knowledge and auditability. This blueprint covers the full production RAG stack — from document ingestion through retrieval, generation, evaluation and governance.
When to Use This Blueprint
- Enterprise knowledge management and document Q&A systems
- Internal policy, legal or compliance assistants
- Customer-facing AI systems requiring factual accuracy
- Regulated industries requiring citation and audit trails
- Multi-source knowledge systems with access controls
Architecture Components
Document Ingestion Pipeline
Structured ingestion from PDFs, SharePoint, databases, APIs. Format-aware parsing, metadata extraction, document versioning.
Chunking Strategy Layer
Semantic chunking vs fixed-size. Sentence-window, recursive character splitting, late chunking for long-form documents.
Embedding Model Selection
Domain-adapted embedding models. Evaluation benchmarks for retrieval quality. Bi-encoder vs cross-encoder tradeoffs.
Vector Store Architecture
Managed (Pinecone, Weaviate) vs self-hosted (Milvus, Chroma). Index types, HNSW parameters, namespace management.
Hybrid Search Layer
Dense (semantic) + sparse (BM25) retrieval. Reciprocal Rank Fusion. Query routing based on intent classification.
Re-Ranking Stage
Cross-encoder re-ranking for precision. Cohere Rerank, ColBERT, BGE-Reranker. Latency vs accuracy tradeoffs.
Context Assembly & Prompting
Context window management. System prompt engineering for grounding. Lost-in-the-middle mitigation strategies.
Generation & Citation Layer
Source citation, verbatim quote detection, hallucination controls. Structured output for downstream processing.
Decision Framework
Decision: Chunking Strategy
Decision: Vector Store
Decision: Retrieval Strategy
Implementation Phases
Week 1–2
Data Audit & Architecture Design
Document inventory, quality assessment, access control mapping, vector store selection, infrastructure design.
Week 3–5
Ingestion Pipeline Build
Parser development, chunking strategy selection, embedding pipeline, vector index build.
Week 6–8
Retrieval & Generation
Hybrid search implementation, re-ranker integration, prompt engineering, citation layer.
Week 9–10
Evaluation & Observability
RAGAS evaluation setup, monitoring dashboards, ground truth creation, performance benchmarking.
Governance Controls
Key Metrics to Track
Need Help Implementing This Blueprint?
Our AI architects can design and implement this architecture for your organisation — governance-first, production-grade and aligned to your specific requirements.