NZAI-AI-ADV-003
Prompting Strategy & Responsible AI
Advanced Prompt Engineering: From Prompting Strategy to Responsible AI is a practical, hands-on course designed to help learners move beyond basic AI interactions and develop the skills needed to produce reliable, high-quality, and ethically sound AI outputs. It covers core techniques such as Chain-of-Thought prompting, persona-based role-play, self-reflection, and guardrails, before advancing into critical topics like hallucination mitigation, bias detection, performance evaluation, and output validation. Learners will develop both the technical proficiency and the responsible mindset required to design, test, and continuously refine AI interactions for real-world use.
Mode of Delivery:
Self-Paced (Online)
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Duration
1-2 Weeks
Format
Self-Paced (Online)
Who Should Join
Technical, Non-Technical, Business People, Consultants, Students, Executives, Professionals, Experts, Teachers.
Resources
Prompt Gallery, Prompt Templates, Explainer Videos, Lesson Plan, Interactive Activities, Practice ,Test & Quizzes, Glossary
Objective
This course aims to equip learners with advanced prompt engineering skills to design, control, evaluate, and continuously improve AI-generated outputs. It focuses on helping learners apply techniques such as Chain-of-Thought prompting, persona-based prompting, role-play, self-reflection, iterative refinement, guardrails, and negative prompting to produce more accurate, relevant, safe, and context-aware responses. The course also builds the learner’s ability to detect hallucinations and bias, define performance metrics, refine prompts through structured feedback loops, and validate AI outputs using external sources, automated checks, and human-in-the-loop review. Overall, the course prepares learners to use AI responsibly and effectively by combining creative prompting techniques with rigorous evaluation, safety, and trust-building practices.
Curriculum
Advanced Prompt Engineering: From Prompting Strategy to Responsible AI course will cover the given below modules.
Module 1: Advanced Prompting Strategies
1.1 Chain-Of-Thought
1.2 Persona-based prompting
1.3 Iterative Refinement
1.4 Guardrails & Negative Prompting
Module 2: Evaluation & Refinement
2.1 Hallucinations & Bias
2.2 Evaluation Metrics
2.3 Iterative Refinement Loop
2.4 Validation Tools
Module 1: Advanced Prompting Strategies
1.1 Chain-Of-Thought
1.2 Persona-based prompting
1.3 Iterative Refinement
1.4 Guardrails & Negative Prompting
Module 2: Evaluation & Refinement
2.1 Hallucinations & Bias
2.2 Evaluation Metrics
2.3 Iterative Refinement Loop
2.4 Validation Tools
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What Will You Learn
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.
Program Delivery & Engagement
This program is delivered as a self-paced online course designed to be completed over 1–2 weeks, allowing learners the flexibility to progress according to their own schedule. The course content is organized into structured modules that include concise lessons in the form of Lesson Notes, Explainer Videos, Practice exercises, Quizzes, and Module-end quizzes to reinforce learning. Participants can access the materials anytime, enabling them to revisit concepts, practice prompt creation, and refine their skills at their convenience. Learner support is typically provided through discussion forums, Q&A boards, or asynchronous feedback channels, where participants can ask questions and engage with instructors or peers. This flexible delivery model ensures that learners can balance their learning with other commitments while still gaining hands-on experience.