RAG Architecture for Enterprise Knowledge

Retrieval-augmented generation is now the default architecture for enterprise AI agents that need to answer questions from internal knowledge. Rather than relying on a model's training data, you retrieve relevant documents from your own systems at query time and inject them into the model's context. The model reasons over your data, not over general internet knowledge. The result is answers that are accurate, specific, and attributable.

In practice, building a production-grade RAG system is significantly more complex than the concept suggests. The quality of retrieval determines the quality of generation. Poor retrieval returns documents that are semantically adjacent but not actually relevant, producing confident but wrong answers. Good retrieval requires careful attention to chunking strategy, embedding model selection, vector index design, metadata filtering, and hybrid search architecture.

The Chunking Problem

Every RAG system begins with a decision: how do you split source documents into chunks that can be meaningfully embedded and retrieved? Too small and you lose context. Too large and you dilute relevance. The right chunking strategy is document-type-specific: contracts chunk differently from knowledge base articles, which chunk differently from product specifications. Semantic chunking - splitting on topic boundaries rather than fixed token counts - produces better retrieval quality but requires either LLM-assisted segmentation or well-structured source documents.

For enterprise deployments where document quality varies widely, a hybrid approach works best: semantic chunking where structure permits, with fallback to overlapping fixed-size chunks for unstructured content. Chunk size should be validated empirically against a golden retrieval dataset rather than set by convention.

Hybrid Search: Vector Plus Keyword

Pure vector search retrieves documents by semantic similarity - useful for conceptual queries but unreliable for precise lookups. If a user asks about a specific invoice number, a policy version, or a product SKU, vector similarity may not surface the exact document. Production RAG systems combine both: vector search for semantic queries, keyword search for precise lookups, with a re-ranking model to merge and score the combined result set.

The performance of a RAG system should be measured continuously against a golden dataset of representative queries and expected retrievals. Without evaluation, optimisations are guesswork and regressions are invisible. Organisations that invest in RAG evaluation infrastructure early find that iteration becomes significantly faster and retrieval quality improves systematically rather than anecdotally.