Configuration Sensitivity in Modular AI Systems
Performance of RAG and GraphRAG systems depends heavily on a wide range of configuration choices, chunk size, retriever type, top-k thresholds, prompt templates, and these interactions become more complex as pipelines grow more modular.
Default configurations, even when manually selected and “reasonable,” consistently underperform tuned configurations by significant margins. In one study, correctness scores improved 62-71% through systematic parameter optimization alone, with no architectural changes.
The implication: in complex modular systems, how you configure matters as much as what you build.
Related:, 05-molecule—rag-architecture-taxonomy, 05-atom—evaluation-metric-limitations