Learning Outcomes

In this free course, you will learn how to:

• Define the core concepts, scope, and terminology involved in prompt engineering and generative AI models
• Explain the way tokens, context windows, temperature, and top-p affect language model outputs and behavior
• Identify key use-cases and limitations of prompt engineering across various industries and domains
• Distinguish between system prompts, user prompts, and techniques for setting clear instructions effectively
• Generate prompts with tailored tone, style, and persona alignment to suit diverse audience requirements
• Assess ambiguity, token budgeting, and validation checks to improve prompt reliability and accuracy
• Apply chain-of-thought strategies to design stepwise prompts that encourage transparent reasoning paths
• Compare methods for dialogue state tracking, slot filling, and session summarization in multi-turn designs
• Create programmatic prompts using template variables, conditional logic, and automation tools efficiently
• Evaluate prompt performance through A/B testing, cost optimization, and human feedback iterations
• Analyze applications of prompt engineering in content generation, data tasks, and specialized domains
• Discuss ethical concerns including bias, privacy, copyright, and hallucination management in AI systems