Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs
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 framework...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/155634 |
| _version_ | 1826210520198283264 |
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| author | Sulaiman, Azfar |
| author2 | Raghavan, Manish |
| author_facet | Raghavan, Manish Sulaiman, Azfar |
| author_sort | Sulaiman, Azfar |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:51:16Z |
| format | Thesis |
| id | mit-1721.1/155634 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:51:16Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1556342024-07-11T03:14:47Z Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs Sulaiman, Azfar Raghavan, Manish Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science System Design and Management Program. 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. 2024-07-10T20:20:50Z 2024-07-10T20:20:50Z 2024-05 2024-06-11T19:47:45.856Z Thesis https://hdl.handle.net/1721.1/155634 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Sulaiman, Azfar Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs |
| title | Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs |
| title_full | Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs |
| title_fullStr | Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs |
| title_full_unstemmed | Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs |
| title_short | Blueprinting AI Economics: Cost Assessment Framework for Business Stakeholders to Navigate Key Aspects in Prompt Engineering, Prompt Automation, and Fine-tuning LLMs |
| title_sort | blueprinting ai economics cost assessment framework for business stakeholders to navigate key aspects in prompt engineering prompt automation and fine tuning llms |
| url | https://hdl.handle.net/1721.1/155634 |
| work_keys_str_mv | AT sulaimanazfar blueprintingaieconomicscostassessmentframeworkforbusinessstakeholderstonavigatekeyaspectsinpromptengineeringpromptautomationandfinetuningllms |