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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-67686 |
| Date | January 2024 |
| Creators | Andersson, Henrik |
| Publisher | Mälardalens universitet, Akademin för innovation, design och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.002 seconds