The article discusses a small adversarial test set designed to detect retrieval failures in Retrieval-Augmented Generation (RAG) pipelines that typical evaluation sets might miss. It emphasizes the importance of proactively testing your own RAG system to uncover hidden weaknesses before users encounter them. By using this targeted test set, developers can improve the reliability and robustness of their RAG models.
By Sara Nobrega
Enterprise Document Intelligence [Vol. 1 #4bis] - A coauthor note on the brick-by-brick pitfalls that justified the four-brick split, before Part II walks the fixes The post 10 Common RAG Mistakes We Keep Seeing in Production appeared first on Towards Data Science .
By Kezhan Shi
The article outlines a framework for constructing Retrieval-Augmented Generation (RAG) pipelines that progressively add complexity as needed to address observed failure modes. It begins with basic lexical and hybrid search techniques, then incorporates reranking and agentic information‑seeking strategies to improve performance. The approach emphasizes that more sophisticated components should only be introduced when simpler methods prove insufficient.
By Tahreem Rasul
Enterprise Document Intelligence [Vol. 1 #7quinquies] - Hallucination is usually garbage-in.
By Kezhan Shi
The article outlines five principles that guide the successful deployment of enterprise agent systems, illustrated with a real-world example from a $100M+ company. It explains how these principles help ensure that such systems can be trusted, verified, and improved over time. The post serves as a practical guide for building reliable agent-based solutions in production environments.
By Sheila Teo
arXiv:2608. 03866v1 Announce Type: new Abstract: This white paper presents ADMITBench, a reference framework for evaluating industrial LLM advisories at the level of the proposed action.
By Yash Misra, Javal Vyas, Siddharth Gutta, Mehmet Mercang\"oz