RAG Architecture Patterns That Actually Scale: Lessons from 50+ Deployments

Retrieval-Augmented Generation has become the de facto standard for building knowledge-intensive AI applications. But not all RAG implementations are created equal.

Beyond Basic RAG

The simplest RAG pattern — embed documents, retrieve similar chunks, generate response — works for demos but breaks down at scale. Production RAG systems require careful attention to several dimensions.

Chunking Strategies

Document chunking is deceptively complex. Fixed-size chunks ignore semantic boundaries. Sentence-level chunks lose context. The optimal strategy depends on your document types and query patterns.

Hybrid Search

Pure semantic search fails on keyword-specific queries. Pure keyword search fails on conceptual queries. The best systems combine both approaches with intelligent routing.

Re-ranking

Initial retrieval is a recall-optimized operation. Adding a re-ranking step with a cross-encoder model dramatically improves precision without sacrificing recall.

Evaluation Frameworks

You can't improve what you don't measure. Robust RAG systems include automated evaluation pipelines that measure retrieval quality, answer accuracy, and user satisfaction across representative query sets.