2026-09-19T01:18:41Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1556342024-07-11T03:14:47Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Raghavan, Manish
Sulaiman, Azfar
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
System Design and Management Program.
2024-07-10T20:20:50Z
2024-07-10T20:20:50Z
2024-05
2024-06-11T19:47:45.856Z
https://hdl.handle.net/1721.1/155634
The rapid proliferation of large language models (LLMs) has led to an intense focus on achieving unprecedented performance benchmarks, often at the expense of considering the substantial computational costs involved. This oversight is compounded by the lack of robust, academically grounded frameworks for comprehensively evaluating these costs, their sources, and strategies for minimization while balancing performance imperatives. To address this critical gap, my research aims to develop a rigorous and systematic framework that enables researchers and industry stakeholders to understand and contextualize the cost implications of fine-tuning, prompt engineering, and prompt automation techniques. By offering a systematic approach to evaluating the trade-offs between performance, cost, and societal impact, this research seeks to advance the practical and sustainable adoption of LLMs across diverse applications.
S.M.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Master of Science in Engineering and Management
Publication
unknown
unknown
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<Abstract>The rapid proliferation of large language models (LLMs) has led to an intense focus on achieving unprecedented performance benchmarks, often at the expense of considering the substantial computational costs involved. This oversight is compounded by the lack of robust, academically grounded frameworks for comprehensively evaluating these costs, their sources, and strategies for minimization while balancing performance imperatives. To address this critical gap, my research aims to develop a rigorous and systematic framework that enables researchers and industry stakeholders to understand and contextualize the cost implications of fine-tuning, prompt engineering, and prompt automation techniques. By offering a systematic approach to evaluating the trade-offs between performance, cost, and societal impact, this research seeks to advance the practical and sustainable adoption of LLMs across diverse applications.</Abstract>
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