RAG Patterns for Enterprise AI Applications

Retrieval-Augmented Generation (RAG) has become the dominant architecture for building enterprise AI applications that need to operate on private, domain-specific data.

The fundamental pattern involves three stages: document ingestion and embedding, semantic retrieval, and context-augmented generation. Each stage presents unique challenges and optimisation opportunities.

Chunking strategy is critical. Too large and you lose semantic precision; too small and you lose context. We recommend a hybrid approach using semantic chunking with configurable overlap boundaries.

Vector database selection impacts performance significantly. For most enterprise use cases, a combination of dense vectors for semantic search and sparse vectors for keyword matching — known as hybrid search — delivers the best recall and precision.

Guardrails are essential for production RAG systems. Citation tracking ensures generated responses can be verified against source documents, while confidence scoring helps users understand when the system is uncertain.

The future of enterprise RAG lies in agentic architectures where AI agents can autonomously decide which retrieval strategies to employ based on query complexity and available data sources.