Poster (Outreach)

FZJ-2026-00874

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png From Parsing to Embeddings: The Hidden Challenges of RAG Development

Haguet, V. (Corresponding author)FZJ* ; Schmidt, M. S.FZJ* ; Niesel, F.FZJ*

2025

This record in other databases:  

Please use a persistent id in citations: doi:10.34734/FZJ-2026-00874

Abstract: Retrieval-Augmented Generation (RAG) is emerging as a powerful method for improving the accuracy and relevance of AI-generated responses by combining information retrieval with large language models (LLMs). In this project, we explore how RAG can be leveraged to build an intelligent chatbot that assists users in navigating high-performance computing (HPC) documentation.Our chatbot is designed to dynamically retrieve information from documentation related to the supercomputers at Forschungszentrum Jülich (JUWELS, JURECA, JUSUF). Developing an efficient RAG-based application presents several challenges, including properly parsing documentation in various formats, effectively segmenting the parsed text into meaningful chunks, and selecting optimal models for retrieval and generation.This work contributes to a broader understanding of RAG’s capabilities and limitations in specialized technical domains, offering insights into its potential for improving user support in complex computing environments.

---

Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)

Appears in the scientific report 2025

---

Database coverage:

---