# A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture arXiv: https://arxiv.org/abs/2309.01105 ยท HTML: https://mlsanity.com/paper/2309.01105/ ## Key facts - Cited by 28 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 2 of its 38 references that are in the map. - First posted on arXiv 3 Sep 2023. ## Placed in - Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/) ## Builds on (most cited first, 2 of 2) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) ## Built on by (most cited first, 28 of 28) - [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024) - [FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs](https://mlsanity.com/paper/2401.03868/) (2024) - [Fine-tuning and Utilization Methods of Domain-specific LLMs](https://mlsanity.com/paper/2401.02981/) (2024) - [Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations](https://mlsanity.com/paper/2404.10779/) (2024) - [Evaluating LLM Metrics Through Real-World Capabilities](https://mlsanity.com/paper/2505.08253/) (2025) - [Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top](https://mlsanity.com/paper/2405.15452/) (2024) - [A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph](https://mlsanity.com/paper/2407.19994/) (2024) - [A Study on the MCP x A2A Framework for Enhancing Interoperability of LLM-based Autonomous Agents](https://mlsanity.com/paper/2506.01804/) (2025) - [MultiDocFusion: Hierarchical and Multimodal Chunking Pipeline for Enhanced RAG on Long Industrial Documents](https://mlsanity.com/paper/2604.12352/) (2026) - [Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform](https://mlsanity.com/paper/2501.00750/) (2025) - [E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing](https://mlsanity.com/paper/2505.20733/) (2025) - [Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models](https://mlsanity.com/paper/2412.17964/) (2024) - [HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering](https://mlsanity.com/paper/2605.29606/) (2026) - [SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest](https://mlsanity.com/paper/2407.01110/) (2024) - [A Study on the Framework for Evaluating the Ethics and Trustworthiness of Generative AI](https://mlsanity.com/paper/2509.00398/) (2025) - [The future of document indexing: GPT and Donut revolutionize table of content processing](https://mlsanity.com/paper/2403.07553/) (2024) - [LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG](https://mlsanity.com/paper/2411.04476/) (2024) - [Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents](https://mlsanity.com/paper/2412.17942/) (2024) - [Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases](https://mlsanity.com/paper/2506.13805/) (2025) - [Data Quality Challenges in Retrieval-Augmented Generation](https://mlsanity.com/paper/2510.00552/) (2025) - [GRAPH-GRPO-LEX: Contract Graph Modeling and Reinforcement Learning with Group Relative Policy Optimization](https://mlsanity.com/paper/2511.06618/) (2025) - [Predictive Prototyping: Evaluating Design Concepts with ChatGPT](https://mlsanity.com/paper/2601.12276/) (2026) - [EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context](https://mlsanity.com/paper/2407.04472/) (2024) - [Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers](https://mlsanity.com/paper/2502.15722/) (2025) - [Accodemy: AI Powered Code Learning Platform to Assist Novice Programmers in Overcoming the Fear of Coding](https://mlsanity.com/paper/2503.16486/) (2025) - [LLM Chatbot-Creation Approaches](https://mlsanity.com/paper/2509.13326/) (2025) - [Modeling User-System Behavior for Training-free Building of Private Domain Conversational Agents](https://mlsanity.com/paper/2511.10283/) (2025) - [M3DocDep: Multi-modal, Multi-page, Multi-document Dependency Chunking with Large Vision-Language Models](https://mlsanity.com/paper/2605.18774/) (2026) ## Abstract (arXiv) This study presents a method for implementing generative AI services by utilizing the Large Language Models (LLM) application architecture. With recent advancements in generative AI technology, LLMs have gained prominence across various domains. In this context, the research addresses the challenge of information scarcity and proposes specific remedies by harnessing LLM capabilities. The investigation delves into strategies for mitigating the issue of inadequate data, offering tailored solutions. The study delves into the efficacy of employing fine-tuning techniques and direct document integration to alleviate data insufficiency. A significant contribution of this work is the development of a Retrieval-Augmented Generation (RAG) model, which tackles the aforementioned challenges. The RAG model is carefully designed to enhance information storage and retrieval processes, ensuring improved content generation. The research elucidates the key phases of the information storage and retrieval methodology underpinned by the RAG model. A comprehensive analysis of these steps is undertaken, emphasizing their significance in addressing the scarcity of data. The study highlights the efficacy of the proposed method, showcasing its applicability through illustrative instances. By implementing the RAG model for information storage and retrieval, the research not only contributes to a deeper comprehension of generative AI technology but also facilitates its practical usability within enterprises utilizing LLMs. This work holds substantial value in advancing the field of generative AI, offering insights into enhancing data-driven content generation and fostering active utilization of LLM-based services within corporate settings.