The deployment of Retrieval Augmented Generation (RAG) systems brings several challenges. The experience report titled Seven Failure Points When Engineering a Retrieval Augmented Generation System shares insights from three case studies in research, education, and biomedicine sectors.

• Highlights seven specific failure points in RAG system development.
• Emphasizes the role of operational validation and evolutionary robustness in RAG systems.
• Identifies the limitations of RAG due to its dual reliance on information retrieval and LLMs.
• Provides learnings on semantic search capabilities and metadata annotation avoidance.

The paper concludes that a RAG system’s quality can not be fully pre-defined and requires continuous evolution post-deployment. The takeaways aim to aid software engineers by presenting potential pitfalls and encouraging the design of more resilient systems. As RAG technologies advance, recognizing these failure points will be vital for effective implementations and could guide future research in making RAG systems more fault-tolerant.