2024 SevenFailurePointsWhenEngineeri
- (Barnett et al., 2024) ⇒ Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu, Zach Brannelly, and Mohamed Abdelrazek. (2024). “Seven Failure Points When Engineering a Retrieval Augmented Generation System.” doi:10.48550/arXiv.2401.05856
Subject Headings: Retrieval Augmented Generation-based System.
Notes
- The paper titled "Seven Failure Points When Engineering a Retrieval Augmented Generation System" provides an insightful experience report on the challenges associated with building RAG systems. It discusses how RAG systems serve as a solution to overcome limitations of using Large Language Models (LLMs) directly.
- The paper explains the operational mechanics of RAG systems, focusing on the Index process and Query process which involve document chunking, embedding, indexing, retrieving embeddings, and generating answers. RAG-based Systems involve two key processes: document indexing (including chunking, embedding generation, vector index creation) and query processing (query embedding, relevant document retrieval, answer generation using an LLM).
- The paper presents findings from three case studies—Cognitive Reviewer, AI Tutor, and a biomedical question-answering system—to demonstrate applications and challenges. It identifies seven distinct failure points in RAG systems:
- FP1 - Missing Content
- FP2 - Missed the Top Ranked Documents
- FP3 - Not in Context - Consolidation strategy Limitations
- FP4 - Not Extracted
- FP5 - Wrong Format
- FP6 - Incorrect Specificity
- FP7 - Incomplete Answers
- The paper shares lessons and considerations for engineering RAG systems, including the significance of effective document chunking and embedding strategies, and the critical need for continuous system calibration.
- The effectiveness of a RAG-based System depends on the quality and relevance of indexed documents, the retrieval system performance, and the LLM's ability to generate accurate answers.
- Testing and monitoring RAG-based Systems is challenging due to LLM reliance and difficulty predicting all user queries. Continuous monitoring is crucial for robustness and reliability.
- The paper proposes future research directions to enhance RAG systems, including exploring chunking and embedding strategies, comparing RAG with finetuning, and establishing software engineering best practices for testing and monitoring.
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
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024 SevenFailurePointsWhenEngineeri | Scott Barnett Stefanus Kurniawan Srikanth Thudumu Zach Brannelly Mohamed Abdelrazek | Seven Failure Points When Engineering a Retrieval Augmented Generation System | 10.48550/arXiv.2401.05856 | 2024 |