‘Seven Failure Points When Engineering a Retrieval Augmented Generation System’ presents an in-depth analysis of the challenges software engineers face when integrating semantic search capabilities into applications through RAG systems. This experience report highlights the lessons learned from three case studies across different domains and enumerates the potential pitfalls one should account for during the design phase.

• Authors: Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu, and others.
• Published: arXiv:2401.05856v1 on January 11, 2024.

Summary:

• RAG systems blend semantic search with LLMs to curate precise answers.
• Despite benefits, RAG systems face failures due to IR systems limitations and reliance on LLMs.
• Seven key failures points identified from case studies within research, education, and biomedical domains.
• Two main takeaways offered: validation is an operational phase process, and robustness evolves over time.

Importance: Highlighting the failure points in RAG systems is essential for refining their design and improving their application across various domains. The insights from this paper serve as a warning and a guideline for software engineers and researchers to better anticipate and address these challenges. It spurs the conversation about the dynamic, evolving nature of RAG system design and the critical need for ongoing validation and adaptability.