# Real-Time RAG for the Identification of Supply Chain Vulnerabilities

arXiv: https://arxiv.org/abs/2509.10469 · HTML: https://mlsanity.com/paper/2509.10469/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 23 Aug 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 7 of 7)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2311.09476/) (2023)
- [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](https://mlsanity.com/paper/2310.06117/) (2023)
- [PaperQA: Retrieval-Augmented Generative Agent for Scientific Research](https://mlsanity.com/paper/2312.07559/) (2023)
- [InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales](https://mlsanity.com/paper/2406.13629/) (2024)
- [How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes](https://mlsanity.com/paper/2409.03454/) (2024)

## Abstract (arXiv)

New technologies in generative AI can enable deeper analysis into our nation's supply chains but truly informative insights require the continual updating and aggregation of massive data in a timely manner. Large Language Models (LLMs) offer unprecedented analytical opportunities however, their knowledge base is constrained to the models' last training date, rendering these capabilities unusable for organizations whose mission impacts rely on emerging and timely information. This research proposes an innovative approach to supply chain analysis by integrating emerging Retrieval-Augmented Generation (RAG) preprocessing and retrieval techniques with advanced web-scraping technologies. Our method aims to reduce latency in incorporating new information into an augmented-LLM, enabling timely analysis of supply chain disruptors. Through experimentation, this study evaluates the combinatorial effects of these techniques towards timeliness and quality trade-offs. Our results suggest that in applying RAG systems to supply chain analysis, fine-tuning the embedding retrieval model consistently provides the most significant performance gains, underscoring the critical importance of retrieval quality. Adaptive iterative retrieval, which dynamically adjusts retrieval depth based on context, further enhances performance, especially on complex supply chain queries. Conversely, fine-tuning the LLM yields limited improvements and higher resource costs, while techniques such as downward query abstraction significantly outperforms upward abstraction in practice.