# Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks

arXiv: https://arxiv.org/abs/2412.08593 · HTML: https://mlsanity.com/paper/2412.08593/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## Placed in

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

## Builds on (most cited first, 14 of 14)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Graph Retrieval-Augmented Generation: A Survey](https://mlsanity.com/paper/2408.08921/) (2024)
- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [Using LLMs in Software Requirements Specifications: An Empirical Evaluation](https://mlsanity.com/paper/2404.17842/) (2024)
- [Rethinking Legal Compliance Automation: Opportunities with Large Language Models](https://mlsanity.com/paper/2404.14356/) (2024)
- [Towards Standards-Compliant Assistive Technology Product Specifications via LLMs](https://mlsanity.com/paper/2404.03122/) (2024)
- [Prioritizing Software Requirements Using Large Language Models](https://mlsanity.com/paper/2405.01564/) (2024)
- [Hybrid Context Retrieval Augmented Generation Pipeline: LLM-Augmented Knowledge Graphs and Vector Database for Accreditation Reporting Assistance](https://mlsanity.com/paper/2405.15436/) (2024)

## Built on by (most cited first, 6 of 6)

- [Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Traces](https://mlsanity.com/paper/2509.23291/) (2025)
- [Evaluating the Use of LLMs for Documentation to Code Traceability](https://mlsanity.com/paper/2506.16440/) (2025)
- [A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance](https://mlsanity.com/paper/2603.09999/) (2026)
- [Neuro-Symbolic Agents for Hallucination-Free Requirements Reuse](https://mlsanity.com/paper/2605.01562/) (2026)
- [Model-Driven Requirements Configuration with Three-Valued Uncertainty Scoring](https://mlsanity.com/paper/2607.26220/) (2026)
- [A Triple-Robustness Analysis of Retrieval-Augmented Generation for Multi-Hop Requirements Traceability](https://mlsanity.com/paper/2608.00705/) (2026)

## Abstract (arXiv)

Ensuring that Software Requirements Specifications (SRS) align with higher-level organizational or national requirements is vital, particularly in regulated environments such as finance and aerospace. In these domains, maintaining consistency, adhering to regulatory frameworks, minimizing errors, and meeting critical expectations are essential for the reliable functioning of systems. The widespread adoption of large language models (LLMs) highlights their immense potential, yet there remains considerable scope for improvement in retrieving relevant information and enhancing reasoning capabilities. This study demonstrates that integrating a robust Graph-RAG framework with advanced prompt engineering techniques, such as Chain of Thought and Tree of Thought, can significantly enhance performance. Compared to baseline RAG methods and simple prompting strategies, this approach delivers more accurate and context-aware results. While this method demonstrates significant improvements in performance, it comes with challenges. It is both costly and more complex to implement across diverse contexts, requiring careful adaptation to specific scenarios. Additionally, its effectiveness heavily relies on having complete and accurate input data, which may not always be readily available, posing further limitations to its scalability and practicality.