Akademik Veri Yönetim Sistemi
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Course
Catalog Description |
Introduction
to Large Language Models (LLM). LLMs for AI-Powered Applications. The copilot
system, frameworks. Choosing an LLM for Application. Closed & Open
Models, Prompt Engineering. Embedding LLMs within Applications. Building
Conversational Applications. Developing the front-end. |
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Course
Objectives |
This
course aims to equip students with a foundational understanding of Large
Language Models (LLMs) and their pivotal role in AI-powered applications.
Students will explore various LLM frameworks and systems, assessing their
capabilities and limitations. They will learn criteria and methodologies for
selecting suitable LLMs tailored to specific application requirements, and
differentiate between closed and open LLM models while applying effective
prompt engineering strategies. The course will foster proficiency in
seamlessly integrating LLMs within diverse application environments and
develop skills in constructing conversational applications using LLM
technology. Additionally, students will gain expertise in designing
user-friendly front-ends that integrate LLM functionalities smoothly. By the
end of this course, participants will possess the theoretical insights and
practical competencies needed to adeptly employ LLMs across varied AI
applications, from enhancing chatbot capabilities to enabling sophisticated
content creation and beyond. |
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Course
Learning Outcomes |
Upon
successful completion of this course, students will be able to:
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Course
Contents |
Introduction
to Large Language Models (LLM). LLMs for AI-Powered Applications. The copilot
system, frameworks. Choosing an LLM for Application. Closed & Open
Models, Prompt Engineering. Embedding LLMs within Applications. Building
Conversational Applications. Developing the front-end. |