Upon completing this course, participants will be able to
- Use Chain-of-Thought prompting to guide AI through step-by-step reasoning, helping improve accuracy, transparency, and performance on complex tasks such as problem-solving, decision-making, technical explanations, and debugging.
- Create persona-based prompts and role-play scenarios that tailor AI responses for specific audiences, tones, styles, and communication goals, making outputs more relevant, engaging, and context-aware.
- Use self-reflection and iterative refinement to improve AI-generated outputs by prompting the AI to critique, revise, and enhance its own responses based on clear quality criteria.
- Apply guardrails and negative prompting to control AI behavior, prevent unwanted or unsafe outputs, maintain relevance, reduce hallucinations, and ensure responses align with ethical and task-specific requirements
- Identify and reduce AI hallucinations by grounding prompts, requesting sources, encouraging uncertainty disclosure, using fact-checking strategies, and avoiding unsupported or invented information.
- Detect and mitigate bias in AI outputs by using inclusive instructions, diverse examples, bias-aware personas, self-correction prompts, and human review practices.
- Gain knowledge of AI performance evaluation methods, including qualitative metrics such as relevance, coherence, accuracy, completeness, style, and safety, as well as quantitative metrics such as exact match, F1 score, BLEU, ROUGE, perplexity, and rating scales.
- Apply a structured iterative prompt refinement loop, using both human feedback and AI-generated feedback to improve prompts and outputs through repeated testing, evaluation, and revision.
- Gain practical knowledge of validation tools and frameworks, including external knowledge sources, automated checks, formatting validators, human-in-the-loop review, AI ethics guidelines, responsible AI frameworks, and MLOps practices.