# Hybrid LLM/Rule-based Approaches to Business Insights Generation from Structured Data

arXiv: https://arxiv.org/abs/2404.15604 · HTML: https://mlsanity.com/paper/2404.15604/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 24 Apr 2024.

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

- [SQL-to-Text Generation with Graph-to-Sequence Model](https://mlsanity.com/paper/1809.05255/) (2018)
- [Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?](https://mlsanity.com/paper/2309.16595/) (2023)
- [A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases](https://mlsanity.com/paper/2311.07509/) (2023)
- [ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting](https://mlsanity.com/paper/2403.14312/) (2024)
- [Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models](https://mlsanity.com/paper/2403.18093/) (2024)
- [A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning](https://mlsanity.com/paper/2403.17254/) (2024)
- [LLMs in HCI Data Work: Bridging the Gap Between Information Retrieval and Responsible Research Practices](https://mlsanity.com/paper/2403.18173/) (2024)

## Built on by (most cited first, 7 of 7)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [An Implementation of Werewolf Agent That does not Truly Trust LLMs](https://mlsanity.com/paper/2409.01575/) (2024)
- [Semantic Layers for Reliable LLM-Powered Data Analytics: A Paired Benchmark of Accuracy and Hallucination Across Three Frontier Models](https://mlsanity.com/paper/2604.25149/) (2026)
- [How to Build AI Agents by Augmenting LLMs with Codified Human Expert Domain Knowledge? A Software Engineering Framework](https://mlsanity.com/paper/2601.15153/) (2026)
- [QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning](https://mlsanity.com/paper/2604.18176/) (2026)
- [Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs](https://mlsanity.com/paper/2511.11125/) (2025)
- [LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing](https://mlsanity.com/paper/2608.16763/) (2026)

## Abstract (arXiv)

In the field of business data analysis, the ability to extract actionable insights from vast and varied datasets is essential for informed decision-making and maintaining a competitive edge. Traditional rule-based systems, while reliable, often fall short when faced with the complexity and dynamism of modern business data. Conversely, Artificial Intelligence (AI) models, particularly Large Language Models (LLMs), offer significant potential in pattern recognition and predictive analytics but can lack the precision necessary for specific business applications. This paper explores the efficacy of hybrid approaches that integrate the robustness of rule-based systems with the adaptive power of LLMs in generating actionable business insights.