Promoting Project Outcomes: A Development Approach to Generative AI and LLM-Based Software Applications’ Deployment
Razieh Akbari Lalaei1, Abbas Mahmoudabadi2
1Razieh Akbari Lalaei, Generative AI Researcher and Developer, Tehran, Iran.
2Dr. Abbas Mahmoudabadi, Ph.D. Director, Master Program in Industrial Engineering, Mehr Astan University, Gilan, Iran.
Manuscript received on 04 July 2024 | Revised Manuscript received on 09 July 2024 | Manuscript Accepted on 15 July 2024 | Manuscript published on 30 July 2024 | PP: 6-13 | Volume-14 Issue-3, July 2024 | Retrieval Number: 100.1/ijsce.D363614040924 | DOI: 10.35940/ijsce.D3636.14030724
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the dynamic realm of artificial intelligence, the emergence of Generative Artificial Intelligence (GAI) has marked a revolutionary stride, particularly in the context of project execution models. This paper delves deep into the sophisticated architectures of GAI, mainly focusing on Large Language Models (LLMs) such as GPT-3 and BERT and their practical applications across varied scenarios. The intricacies of deploying these models have been effectively unraveled to ensure resonating with the specific demands of distinct cases, falling within departmental integration, medical diagnostics, or tailored training modules. Central to the proposed exposition is the innovative “Forward and Back Systematic Approach” designed for executing GAI projects. This approach is meticulously structured to enhance efficiency and ensure a harmonious alignment with the nuanced requirements of diverse applications. We dissect some strategies, including leveraging Private Generalized LLM APIs, in-context learning (ICL), and fine-tuning methodologies, to empower these models to adapt and excel. Furthermore, the proposed platform underscores the pivotal role of evaluation criteria in refining GAI project outcomes, ensuring each model’s prowess. It is not strictly theoretical but yields tangible benefits in real-world applications. Under the aegis of this comprehensive exploration, the result of the study would serve as a beacon for enthusiasts and professionals navigating the GAI landscape by offering insights into optimizing robust models for specific and case-driven utilities. Standing on the brink of a modern era in AI, this paper contributes a substantial framework and critical analysis, steering the course for future innovations and applications of GAI.
Keywords: Generative AI, Large Language Models, LLM-Based Software Applications, Private Generalization, Monitoring.
Scope of the Article: Computer Science and Its Applications