Towards Explainable RAG - Interpreting the Influence of Retrieved Passages on Generation: Deployment and Systems Integration

Authors

  • Lance Stewart Author
  • Mitchell Phillips Author
  • Parker Bennett Author

Keywords:

reliable retrieval-augmented generation, deployment constraints and systems integration, evidence synthesis, reproducibility, research evaluation

Abstract

Operational value depends on latency, resource use, interface dependencies, and reliability within the systems that must host the method. This structured evidence review evaluates "Towards Explainable RAG: Interpreting the Influence of Retrieved Passages on Generation" alongside nine author-disjoint, topically matched publications in reliable retrieval-augmented generation. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through deployment constraints and systems integration, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda tests end-to-end latency, failure recovery, resource demand, and compatibility with existing operational interfaces.

References

  1. Sang, Y. (2025). Towards Explainable RAG: Interpreting the Influence of Retrieved Passages on Generation. 2025 4th International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), 397-400. https://doi.org/10.1109/raiic65850.2025.11170170
  2. Hassan, S.-B., Abdullah,, & Abbas, M. (2026). Agentic Self-RAG: Multi-Agent Reasoning for Self-Correcting Retrieval-Augmented Generation. 2026 International Conference on IT and Industrial Technologies (ICIT), 1-6. https://doi.org/10.1109/icit68548.2026.11577708
  3. Fasolino, I. (2026). In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning. . https://doi.org/10.32388/7hxkqe
  4. Samal, M.-P. (2026). A Theoretical Analysis of Self-Contained Retrieval-Augmented Generation with Small Language Models. . https://doi.org/10.36227/techrxiv.177219989.96070478/v1
  5. Pi, W. (2024). Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG). . https://doi.org/10.59350/q2pq3-0fv85
  6. Xue, X., Zhang, G., Jiang, L., & Liu, C. (2025). A Comparative Study of Retrieval-Augmented Generation, Graph Retrieval-Augmented Generation, and Fine-Tuned Large Language Models for Fire Engineering Knowledge Retrieval. . https://doi.org/10.2139/ssrn.5316632
  7. Bose, R. (2025). Introduction to Retrieval-Augmented Generation (RAG). Mastering Retrieval-Augmented Generation, 3-32. https://doi.org/10.1007/979-8-8688-1808-0_1
  8. Zhai, W. (2025). SAM-RAG: An Self-adaptive Framework for Multimodal Retrieval-Augmented Generation. 2025 International Joint Conference on Neural Networks (IJCNN), 1-8. https://doi.org/10.1109/ijcnn64981.2025.11227819
  9. Gudipati, S.-K., Mishra, N.-A., Ankam, G., Akkisetti, S.-R., Mohammed, A., & Ponugoti, S. (2026). CityCopilot-X: A Real-Time Explainability Panel for Retrieval-Augmented Generation (RAG). 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), 1-9. https://doi.org/10.1109/icaic67076.2026.11395791
  10. Kau, A. (2024). Understanding Retrieval Pitfalls: Challenges Faced by Retrieval Augmented Generation (RAG) models. . https://doi.org/10.59350/xcq3s-jvk04

Downloads

Published

2026-08-27

Issue

Vol. 1 (2026): Archive

Section

Review Article

License

Copyright (c) 2026 Machine Intelligence & Responsible Systems

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.