AIDU-PROMPT-203
Delivery Type: Live, instructor-led Remote or In person
Prerequisite: Foundation Models, LLMs & Multimodal AI
This course provides professionals with a practical, non-technical framework for using AI tools effectively through clear, structured prompting. It is designed for professionals who rely on AI for writing, research, analysis, planning, and decision support, and want better results, fewer errors, and greater control.
Rather than teaching prompt “tricks” or model-specific hacks, the course builds durable mental models for how AI systems interpret instructions, context, and constraints. Participants learn why vague prompts fail, how ambiguity leads to hallucinations or misleading outputs, and how structured prompting improves reliability across tasks.
Prompting is treated as a professional skill, not a technical one. The course emphasizes task framing, boundary setting, safe iteration, and output validation, with special attention to common workplace failure modes such as over-trust, silent errors, and productivity loss from poorly designed interactions.
Core Topics:
How AI systems interpret prompts and instructions
Task framing and goal clarity
Constraints, assumptions, and scope control
Prompting versus delegation and decision ownership
Iterative prompting and refinement
Detecting hallucinations and silent errors
Prompting for common professional tasks
Validation and verification techniques
Productivity pitfalls and overuse
Outcomes:
Understand how AI systems interpret prompts and context
Recognize why common prompting approaches fail
Structure prompts to clarify goals, scope, and constraints
Use iteration and follow-up prompts effectively
Reduce hallucinations and misleading outputs
Validate and sanity-check AI-generated content
Apply prompting techniques to writing, research, and analysis
Maintain appropriate human judgment and accountability
