RAG Architecture Patterns: Beyond Basic Retrieval
Advanced techniques for building RAG systems that actually answer questions correctly — chunking strategies, reranking, and evaluation frameworks.
RAG Done Right
Most RAG implementations retrieve documents, stuff them into a prompt, and hope for the best. This works for demos. In production, with messy real-world documents and users who ask ambiguous questions, naive RAG fails constantly.
The gap between "retrieves relevant documents" and "answers questions correctly" is larger than most teams realize. Retrieval is necessary but not sufficient. The quality of answers depends on chunking strategies, query understanding, reranking, and careful prompt construction.
This research presents architecture patterns we've refined across dozens of production RAG deployments, from simple Q&A systems to complex multi-source research assistants.
Architecture Insights
Chunking Strategy Impact
Semantic chunking (splitting on meaning boundaries) outperforms fixed-size chunks by 40% on answer accuracy. Chunk size should match your query patterns, not arbitrary token limits.
Reranking ROI
Adding a cross-encoder reranker improves answer quality by 35% with minimal latency impact. Retrieve more, rerank down to fewer. This is the highest-ROI improvement for most RAG systems.
Query Transformation
Expanding user queries with hypothetical answers (HyDE) or multiple query variations improves recall by 50% for complex questions. Simple keyword matching misses semantic intent.
Eval-Driven Development
Teams that build evaluation datasets before optimizing achieve production-quality RAG 2x faster. You can't improve what you don't measure.
Implementation Deep Dive
The Chunking Hierarchy
We use a three-level chunking strategy: documents split into sections (preserving headers/structure), sections split into semantic paragraphs, and paragraphs indexed with parent references. At retrieval time, we fetch paragraphs but expand context by including parent sections when needed. This balances precision and context.
Query Understanding Pipeline
Before retrieval, process queries through: intent classification (is this a factual lookup, comparison, or synthesis task?), entity extraction (what specific things are being asked about?), and query expansion (what related terms or phrasings might help?). Different query types need different retrieval strategies.
The Reranking Layer
Initial retrieval casts a wide net — fetch 20-50 candidates with a fast bi-encoder. Then rerank with a cross-encoder that sees query and document together. Finally, apply diversity filtering to avoid redundant information. This three-stage pipeline consistently outperforms single-stage retrieval.
Building Your Eval Dataset
Start with 100 real user questions, manually annotate correct answers and source passages. Use this golden dataset to measure retrieval recall, answer accuracy, and faithfulness (does the answer reflect what's in the sources?). Automate eval runs in CI — every change should be measured against this baseline.
Build RAG That Actually Works
Let us help you design and implement a RAG system that answers questions correctly, not just retrieves documents.