Prompt Engineering

Course Outcomes

Upon successful completion of this course, students will be able to:

• CO1 (Knowledge/Remember): Recall and describe the fundamental concepts of prompt engineering, generative artificial intelligence, large language models (LLMs), prompt structures, and AI-assisted content generation.

• CO2 (Understanding): Explain and interpret the principles of prompt design techniques, prompt optimization, context engineering, AI model behavior, and ethical considerations in generative AI.

• CO3 (Application): Apply prompt engineering techniques and AI tools to design, refine, and optimize prompts for tasks such as content generation, code generation, data analysis, summarization, translation, and problem-solving.

• CO4 (Analysis): Analyze and compare different prompting strategies, AI model responses, evaluation metrics, and optimization methods to improve the accuracy, reliability, and efficiency of AI-generated outputs.

• CO5 (Synthesis/Evaluation): Design, implement, and evaluate effective, ethical, inclusive, and sustainable AI-driven solutions by integrating advanced prompt engineering techniques, responsible AI practices, and domain-specific knowledge to address real-world challenges.