2024 SevenFailurePointsWhenEngineeri

From GM-RKB
Jump to navigation Jump to search

Subject Headings: Retrieval Augmented Generation-based System.

Notes

Cited By

Quotes

Abstract

Software engineers are increasingly adding semantic search capabilities to applications using a strategy known as Retrieval Augmented Generation (RAG). A RAG system involves finding documents that semantically match a query and then passing the documents to a large language model (LLM) such as ChatGPT to extract the right answer using an LLM. RAG systems aim to: a) reduce the problem of hallucinated responses from LLMs, b) link sources / references to generated responses, and c) remove the need for annotating documents with meta-data. However, RAG systems suffer from limitations inherent to information retrieval systems and from reliance on LLMs. In this paper, we present an experience report on the failure points of RAG systems from three case studies from separate domains: research, education, and biomedical. We share the lessons learned and present 7 failure points to consider when designing a RAG system. The two key takeaways arising from our work are: 1) validation of a RAG system is only feasible during operation, and 2) the robustness of a RAG system evolves rather than designed in at the start. We conclude with a list of potential research directions on RAG systems for the software engineering community.

References

;

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2024 SevenFailurePointsWhenEngineeriScott Barnett
Stefanus Kurniawan
Srikanth Thudumu
Zach Brannelly
Mohamed Abdelrazek
Seven Failure Points When Engineering a Retrieval Augmented Generation System10.48550/arXiv.2401.058562024