Where does your RAG pipeline actually fail, retrieval or generation?

Reddit r/AI_Agents News

Summary

The article discusses the challenge of distinguishing between retrieval and generation failures in RAG systems and explores practical methods to measure each component independently.

Something I keep running into when debugging RAG systems: teams report the model hallucinating, and the model turns out to be fine. The chunk it needed never came back from retrieval, so it answered from priors. The two failures look identical from the outside. Same symptom, opposite fix. What I'm curious about is how people here separate them in practice. Logging retrieved chunks alongside every answer catches some of it, but that still needs someone reading logs. Measuring retrieval recall separately needs labelled data most teams do not have. So: do you measure retrieval quality independently of answer quality, and if so, how? Or do you treat the pipeline as one number and tune until the output looks right?
Original Article

Similar Articles

Most RAG apps in production are confidently wrong and nobody talks about this enough

Reddit r/ArtificialInteligence

The article highlights a critical failure mode in production RAG systems where confident but incorrect answers arise from versioning issues and lack of uncertainty mechanisms. It proposes architectural improvements like routing layers, retrieval scoring, and hallucination checks to mitigate these errors.

Why Retrieval-Augmented Generation Fails: A Graph Perspective

arXiv cs.CL

This paper investigates why Retrieval-Augmented Generation (RAG) systems fail despite having access to correct evidence. Using circuit tracing and attribution graphs, the authors find that correct predictions exhibit deeper reasoning paths and more distributed evidence flow, while failures show shallow and fragmented patterns. They propose a graph-based error detection framework and targeted interventions to improve RAG reliability.