Generative AI Applied to Technology Strategy - Using Open-source Large Language Models In an Agentic Way

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dc.contributor.authorSörstrand, Elias
dc.contributor.authorWang, Junchao
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineeringen
dc.contributor.examinerDamaschke, Peter
dc.contributor.supervisorHaghir Chehreghani, Morteza
dc.date.accessioned2025-01-10T09:56:32Z
dc.date.available2025-01-10T09:56:32Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractThis 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.
dc.identifier.coursecodeDATX05
dc.identifier.urihttp://hdl.handle.net/20.500.12380/309070
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectArtificial intelligence
dc.subjectAI
dc.subjectgenerative AI
dc.subjectgenerative artificial intelligence
dc.subjectgenAI
dc.subjectmachine learning
dc.subjectlanguage agents
dc.subjectDeep learning
dc.subjectNatural language processing
dc.subjectNLP
dc.titleGenerative AI Applied to Technology Strategy - Using Open-source Large Language Models In an Agentic Way
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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