mlsanityData to 14 Oct 2026

Building Specialized Software-Assistant ChatBot with Graph-Based Retrieval-Augmented Generation

arXiv 2511.05297 ↗First posted 7 Nov 2025cs.SE cs.LGcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Digital Adoption Platforms (DAPs) have become essential tools for helping employees navigate complex enterprise software such as CRM, ERP, or HRMS systems. Companies like LemonLearning have shown how digital guidance can reduce training costs and accelerate onboarding. However, building and maintaining these interactive guides still requires extensive manual effort. Leveraging Large Language Models as virtual assistants is an appealing alternative, yet without a structured understanding of the target software, LLMs often hallucinate and produce unreliable answers. Moreover, most production-grade LLMs are black-box APIs, making fine-tuning impractical due to the lack of access to model weights. In this work, we introduce a Graph-based Retrieval-Augmented Generation framework that automatically converts enterprise web applications into state-action knowledge graphs, enabling LLMs to generate grounded and context-aware assistance. The framework was co-developed with the AI enterprise RAKAM, in collaboration with Lemon Learning. We detail the engineering pipeline that extracts and structures software interfaces, the design of the graph-based retrieval process, and the integration of our approach into production DAP workflows. Finally, we discuss scalability, robustness, and deployment lessons learned from industrial use cases.

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 29 of its 47 references that are in the map. First posted on arXiv 7 Nov 2025.

Builds on

29 of 47 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers22 Mar
  2. 2024The Chronicles of RAG: The Retriever, the Chunk and the Generator15 Jan

Other references · 27

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  6. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  7. 2017Graph Attention Networkscited by 8,923
  8. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  9. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  10. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  11. 2024Mixtral of Expertscited by 1,823
  12. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
15 more
  1. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  2. 2023LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 324
  3. 2024Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?cited by 232
  4. 2023GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarkingcited by 210
  5. 2023Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMscited by 178
  6. 2024Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answeringcited by 96
  7. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 88
  8. 2023UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluationcited by 75
  9. 2023Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-Incited by 71
  10. 2023Learning to Retrieve In-Context Examples for Large Language Modelscited by 60
  11. 2021ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoningcited by 58
  12. 2023Making Large Language Models Perform Better in Knowledge Graph Completioncited by 48
  13. 2024Can LLM Graph Reasoning Generalize beyond Pattern Memorization?cited by 47
  14. 2025Re-ranking the Context for Multimodal Retrieval Augmented Generationcited by 11
  15. 2023Furthest Reasoning with Plan Assessment: Stable Reasoning Path with Retrieval-Augmented Large Language Modelscited by 2

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