Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs
Name
sulaiman-azfar-sm-idm-2024-thesis.pdf
Description
Thesis PDF
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3.71 MB
Format
Adobe PDF
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c66b162656cc8f0b0f0edff1d962ebff
Author(s)
Sulaiman, Azfar
Advisor(s)
Raghavan, Manish
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
System Design and Management Program.
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