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RETRIEVAL-AUGMENTEDGENERATION WITH AZURE OPEN AI

This thesis investigates the implementation of an Retrieval-Augmented Generation (RAG) Teamschat bot to enhance the efficiency of a service organization, utilizing Microsoft Azure’s AI services.The project combines the retrieval capabilities of Azure AI Search with OpenAI’s GPT-3.5 Turboand Meta’s Llama 3 70B-instruct. The aim is to develop a chat bot capable of handling bothstructured and unstructured data. The motivation for this work comes from the limitations ofstandalone Large Language Models (LLMs) which often fail to provide accurate and contextuallyrelevant answers without external knowledge. The project uses the retriever and two languagemodels and evaluates them using F1 scoring. The retriever performs well, but the RAG modelproduces wrong or too long answers. Metrics other than F1 scoring could be used, and future workin prompt engineering as well as larger test datasets could improve model performance.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-67686
Date January 2024
CreatorsAndersson, Henrik
PublisherMälardalens universitet, Akademin för innovation, design och teknik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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