RAG 的单项冠军，组队就翻车
把检索增强生成(RAG)的每个环节单独调优，都能看到明显提升；但把它们拼成一条完整流水线，提升就缩水了。研究者用开源框架 LASAGNA 测了 450 种组合，发现三个单项技术各自预测能带来 47% 的改进，合在一起实际只有 17%。更麻烦的是，让检索更准和让答案更忠实，往往需要相反的设计，优化一个可能伤另一个；而且在小语料上最优的组合，换到大语料就不再是最优。这不是说单项优化没用，而是说它测出来的数字在真实场景里不可靠——你明天搭 RAG 系统时，不能照搬论文里某个环节的结论，得把整条链路一起测。LASAGNA 就是为这个而生的。
📄 原文摘要(英文)
Retrieval Augmented Generation (RAG) grounds large language models in external knowledge. Modern pipelines combine embedding, chunking, retrieval, reranking, query augmentation, context compression, and agentic verification, yet these techniques are typically evaluated one at a time. It remains unclear whether their isolated gains survive composition, whether retrieval quality and answer faithfulness favor the same design choices, and whether conclusions from small corpora hold at large scale. We pre-declare three hypotheses and introduce LASAGNA, an open-source framework for controlled RAG evaluation. We test 450 configurations across eight benchmarks and corpora ranging from 5K to 200K documents. Three findings emerge. First, individually strong techniques from different phases of a RAG pipeline do not necessarily compose: three retrieval techniques predict a +47% combined improvement but deliver only +17% when used together. Second, component gains stack differently for retrieval and faithfulness, and optimizing one can hurt the other. Third, the best composition changes as the corpus grows: the combination that wins on a small corpus may not remain best at large scale. These findings show that gains measured in isolation are not reliable predictors of realistic performance, and that new RAG techniques should be tested with the full surrounding stack. We call on researchers to do so and supply LASAGNA to simplify these experiments.