Production RAG Blueprint
A decision-framework approach to RAG — chunking, embeddings, hybrid retrieval, re-ranking, LLM-as-judge evaluation — sized to the use case, not stacked indiscriminately.
RAGVector SearchProduction AILLM-as-JudgeEvaluation
A reference blueprint for production-grade Retrieval-Augmented Generation drawn from real deployments:
- Chunking strategy — semantic, fixed, hierarchical, and when each wins
- Embedding model selection — domain-tuned vs. general-purpose
- Hybrid retrieval — BM25 + dense, metadata filters, query rewriting
- Re-ranking — cross-encoders, LLM re-rankers, HyDE, MQE, GraphRAG
- Evaluation harnesses — LLM-as-judge, golden datasets, retrieval-quality metrics
The blueprint codifies a decision framework: choose techniques based on use case, latency, cost, and risk profile — instead of stacking every optimization “just in case.”