From documentations to intelligence: Retrieval-augmented troubleshooting for telecommunications software

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Volume Title

School of Science | Master's thesis

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Date

2025-12-27

Department

Major/Subject

Computer Science

Mcode

Degree programme

Master's Programme in Computer, Communication and Information Sciences
Master's Programme in Computer, Communication and Information Sciences
Master's Programme in Computer, Communication and Information Sciences

Language

en

Pages

69

Series

Abstract

Managing complex telecommunications software requires navigating technical documentations, often leading to inefficiencies in troubleshooting and deployment. This thesis presents the Retrieval-Augmented Troubleshooting Assistant (RATA) designed to automate technical support by integrating Large Language Models (LLMs) with domain-specific documentations of a modern telecommunication software product, Lightweight real-Time Monitoring and Troubleshooting (LTMT). The research implements a privacy-compliant application that transforms XML-based documentations into semantic vector indices. Various architectural configurations are evaluated and verified, specifically contrasting single-stage versus multi-stage hybrid retrieval and fixed-length versus syntax-aware chunking strategies. Evaluation results from a curated golden dataset of LTMT documentations demonstrate that a multi-stage retrieval approach combined with syntax-based chunking yields superior performance, achieving an average Recall@15 score of nearly 98%. Furthermore, the integration with Llama3.3-70B model proved most effective for intent recognition and structured JSON generation when utilizing n-shot (n >= 2) prompting for LTMT-related questions. These findings validate that specific architectural optimizations are essential for deploying reliable AI assistants in telecommunications environment.

Description

Supervisor

Truong, Linh

Thesis advisor

Nyberg, Kristoffer

Keywords

large language model, retrieval-augmented generation, telecommunications, data enhancement, information retrieval, prompt engineering

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Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202601221845

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[dipl] Perustieteiden korkeakoulu / SCI