Generative AI Applied to Technology Strategy - Using Open-source Large Language Models In an Agentic Way
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Computer science – algorithms, languages and logic (MPALG), MSc
Publicerad
2024
Författare
Sörstrand, Elias
Wang, Junchao
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis explores the application of generative AI in technology strategy, specifically
through the use of open-source Large Language Models (LLMs) to develop an
agent capable of understanding documents and providing insightful analysis.
The research focuses on adapting existing LLMs to automate the extraction, synthesis,
and analysis of external data from diverse sources. By implementing techniques
such as Retrieval-Augmented Generation (RAG), we aim to enhance the accuracy
and reliability of AI-generated content, mitigating issues like hallucination.
The project involves the customization of LLMs for strategic decision-making, the development
of a user-friendly interface, and rigorous evaluation to ensure operational
effectiveness. Our findings indicate that integrating generative AI with strategic
planning processes can significantly improve decision-making efficiency and accuracy,
offering valuable insights for the electrification of road transportation and beyond.
Beskrivning
Ämne/nyckelord
Artificial intelligence , AI , generative AI , generative artificial intelligence , genAI , machine learning , language agents , Deep learning , Natural language processing , NLP