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Title:Konfiguracija produktnega kataloga s pomočjo velikih jezikovnih modelov : diplomsko delo
Authors:ID Balaban, Luka (Author)
ID Potočnik, Božidar (Mentor) More about this mentor... New window
ID Budna, Aleš (Comentor)
Files:.pdf VS_Balaban_Luka_2025.pdf (3,25 MB)
MD5: F44C16790C2EC2ACB0F0A3F93F4768F1
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Zaključno delo obravnava problem kompleksnosti konfiguracije produktnih katalogov v sistemu Monetization podjetja Tridens, kjer se uporabniki pogosto soočajo z izzivi pri razumevanju in nastavitvi zapletenih obračunskih modelov. Cilj dela je razviti umetno inteligenco, ki bo s pomočjo velikih jezikovnih modelov (LLM), kot je ChatGPT, omogočila enostavnejšo in bolj intuitivno konfiguracijo produktnih katalogov. V okviru raziskave smo analizirali obstoječo tehnično dokumentacijo in bazo znanja podjetja Tridens ter izvedli integracijo modela ChatGPT v zaledni sistem za avtomatizirano generiranje in prilagajanje produktnih katalogov. Rešitev smo preizkusili s testnim sklopom povpraševanj, pri čemer smo primerjali rezultate generiranih konfiguracij z referenčnimi primeri. Rezultati so pokazali, da umetna inteligenca uspešno izboljšuje natančnost in hitrost konfiguracije produktnih katalogov, hkrati pa zmanjšuje potrebo po ročnem delu. Zaključek naloge potrjuje, da uporaba velikih jezikovnih modelov prispeva k izboljšanju uporabniške izkušnje in poenostavitvi konfiguracijskih procesov, kar lahko podjetjem, kot je Tridens, prinese pomembne prednosti v konkurenčnem okolju.
Keywords:veliki jezikovni modeli, umetna inteligenca, produktni katalog, chatGPT, java
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[L. Balaban]
Year of publishing:2024
Number of pages:1 spletni vir (1 datoteka PDF (XIII, 50 str.))
PID:20.500.12556/DKUM-91341 New window
UDC:004.8(043.2)
COBISS.SI-ID:227683843 New window
Publication date in DKUM:06.02.2025
Views:533
Downloads:63
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:13.12.2024

Secondary language

Language:English
Title:Product catalog configuration using large language models
Abstract:The thesis addresses the complexity of configuring product catalogs in the Monetization system of Tridens, where users often face challenges in understanding and setting up intricate billing models. The aim of the work is to develop artificial intelligence that, with the help of large language models (LLM) like ChatGPT, will enable easier and more intuitive configuration of product catalogs. As part of the research, we analyzed the existing technical documentation and knowledge base of Tridens and integrated the ChatGPT model into the backend system for automated generation and customization of product catalogs. The solution was tested using a set of queries, comparing the results of the generated configurations with reference examples. The results showed that artificial intelligence successfully improves the accuracy and speed of product catalog configuration while reducing the need for manual work. The conclusion of the thesis confirms that the use of large language models contributes to enhancing the user experience and simplifying configuration processes, which can provide companies like Tridens with significant advantages in a competitive environment.
Keywords:Large Language Models, Artificial Intelligence, Product Catalog, chatGPT, Java


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