Bruckhaus - 2024 - RAG Does Not Work for Enterprises

Intro

implementing RAG effectively in real-world, enterprise settings poses several challenges.
The retriever needs to efficiently search through massive, constantly-updated knowledge bases to find the most relevant information for each query [Karpukhin et al., 2020]
The generator needs to intelligently fuse the retrieved content with its own learned knowledge to produce coherent and accurate outputs [Shao et al., 2023]
the RAG system needs to satisfy stringent requirements around data security, privacy, interpretability, and auditability [Arrieta et al., 2020].

include built-in access controls, anonymization techniques, and auditing mechanisms.
intelligently blending advanced semantic search techniques with hybrid query strategies, advanced RAG solutions retrieve the most relevant and reliable information to augment the generation process
such solutions must provide clear explanations and attributions for its outputs, enabling enterprises to trust and act on the insights with confidence.
flexible, API-driven architecture and pre-built connectors for popular enterprise systems.

Lack of fine-grained control over retrieval and generation processes, which is crucial for ensuring accuracy, consistency, and regulatory compliance [Martorana et al., 2022, Anderljung et al., 2023, Rahwan et al., 2023].
Limited scalability and performance when dealing with massive, heterogeneous enterprise knowledge bases [Ahmad et al. 2019 , Nambiar et al., 2023].
Insufficient explainability and auditability of RAG outputs, which is essential for building trust and accountability in high-stakes enterprise use cases [Eibich et al. 2024, Gao et al. 2024, Kamath & Liu 2021].
Challenges in integrating RAG capabilities into existing enterprise systems and workflows, which often have complex security, governance, and data management requirements.