Equinet Academy
Build prompts that reason, verify, and hold up under pressure.
Course Description
What is This Course About?
This course introduces a systematic approach to prompt engineering built around a single day-long challenge: taking a complex, real-world problem with no single correct answer and moving it from initial exploration through to a structured, verifiable, and ethically bounded AI-assisted strategy.
The day is structured around a three-phase analogy. In the morning, learners are Explorers: using divergent prompt patterns to view the problem through multiple perspectives, surface hidden blind spots, and generate unconventional ideas. In the afternoon, they become Architects: applying Chain-of-Thought reasoning, critical evaluation, Template Pattern blueprinting, and systemic guardrails to harden the morning’s ideas into a rigorous, reusable prompt system. In the final phase, they become Quality Inspectors: stress-testing the system through scenario simulation, applying Tail Generation for persistent output formatting, and preparing a validated prompt solution for stakeholder review.
The course covers more than 10 named prompt patterns across the three phases. Every pattern is applied to the same real problem throughout the day, so learners experience how the patterns interact and compound – not just how each one works in isolation.
Course Analogy Framework
Tools Covered
Inside your toolbox
Work with the same powerful tools the pros trust, practical, proven, and built to help you succeed from day one.
Target Audience
Made for the BOLD!
This course is designed for:
- Business professionals and strategists who use AI for complex analysis, advisory work, or multi-stakeholder problem-solving
- Policy practitioners, consultants, and project leads who need AI outputs that are structured, verifiable, and professionally defensible
- Digital team members completing the Certified AI Practitioner programme who want to advance from tool fluency to systematic prompt engineering
- Anyone who has found that basic prompting produces inconsistent or unreliable outputs for high-stakes tasks
Prerequisites
What You’ll Need to Get Started
- Basic familiarity with at least one AI tool such as ChatGPT, Gemini, or Claude – learners who have used AI for workplace tasks are well-positioned
- Completion of Module 2 (Generative AI) of the Certified AI Practitioner programme, or equivalent introductory AI tool experience
- Recommended pre-read: the 4 problem briefs in the course handout, distributed one week before the course date
No coding or technical background required.
Course Highlights
What You’ll Learn
In this Prompt Engineering course, you will:
- Explore prompting types such as zero-shot, few-shot, conditional, and CoT
- Learn structured techniques to craft, revise, and optimise prompts
- Use AI prompt tuning tools for efficiency and output evaluation
- Design prompts for different use cases, models, and stakeholder needs
- Apply real-world frameworks to evaluate, troubleshoot, and improve AI outputs
Learning Outcomes
What You Will Be Able to Demonstrate
The developer has defined four observable, measurable indicators:
| Learning Outcome | Observable and Measurable Indicator |
| Pattern Fluency | Identify and apply at least 10 different prompt patterns to solve distinct sub-tasks within the day’s wicked problem |
| Reasoning Transparency | Implement Chain-of-Thought or Zero-Shot CoT patterns to eliminate black-box logic and produce step-by-step verifiable reasoning |
| Structural Precision | Produce data in a specific, reusable format using the Template Pattern with explicit placeholders and a mandatory Claim Check footer |
| Systemic Robustness | Create a Root Prompt that maintains immutable guardrails, prevents hallucinations, and grounds responses in domain-specific context |
Skills You Will Acquire
Equip yourself with these skills:
Pattern Fluency
Identify and apply at least 10 different prompt patterns to solve sub-tasks within a complex, multi-stage problem-solving session.
Reasoning Transparency
Implement Chain-of-Thought and Zero-Shot CoT patterns to eliminate black-box logic and make AI reasoning visible and verifiable.
Structural Precision
Produce consistent, reusable outputs using the Template Pattern with explicit placeholders, Claim Check footers, and Meta Language shorthand.
Systemic Robustness
Create Root Prompts with immutable guardrails, Context Injection for domain grounding, and Semantic Filters to prevent undesirable AI outputs.
Divergent Problem Exploration
Apply Persona Pattern, Flipped Interaction, and Alternate Approach techniques to surface hidden blind spots and generate unconventional solution directions.
Convergent Prototype Engineering
Use Outline Expansion, LLM Grading, Prompt Critique, and Task Decomposition to refine and harden a strategic prototype into a verified deliverable.
Simulation-Based Testing
Apply Game Play Pattern, Tail Generation, and multi-turn chaining to stress-test prompt systems and track outcomes across extended AI interactions.
Responsible AI Application
Apply data protection techniques and ethical prompt design practices to produce safe, balanced, and professionally deployable AI outputs.
Certification Track
Level up!
Get certified. Get noticed. Get ahead.
This course is Module 3 of the Certified AI Practitioner (Business and Work Applications) Programme:
Module 1: WSQ AI and Machine Learning
Module 2: Generative AI (ChatGPT, Gemini, and Popular AI Tools)
Module 3: Prompt Engineering – this course
Module 4: Agentic AI
A Certification of Completion by Equinet Academy will be awarded to candidates who have demonstrated competency in the Prompt Engineering course assessment and achieved at least 75% attendance.
Course Outline
Inside the course
The course follows a single-day journey through three integrated phases. Every prompt pattern is applied to the same wicked problem selected in the opening session, so learners experience the full engineering pipeline from divergent exploration through convergent hardening to validated deployment.
Divergent Exploration
- The Wicked Problem – problem selection and framing, two hallmarks of a wicked problem, group share-out
- Perspective Shifting and Uncovering the Unknown – Persona Pattern (3 lenses), Cognitive Verifier, Flipped Interaction with AI-as-interrogator, blind spot documentation
- Possibility Compass – Alternate Approach Pattern, Chunk Up exercise, stakeholder pros/cons matrix, Cognitive Verifier challenge, Tree of Thought introduction
- The Strategy Prototype – Outline Expansion to 3 strategic pillars, Logic Stress Test, Meta Language Creation (1 to 2 shorthand terms)
Convergent Engineering
- Logic and Reasoning – Chain-of-Thought, Zero-Shot CoT, ReAct Prompting as reasoning transparency pattern, Task Decomposition, Question Refinement
- Prompt Critic and Structural Blueprinting – 1-to-10 Comparative Audit with LLM Grading, Pulitzer Persona Critique, Blind Spot Stress Test, Template Pattern with placeholders, Chat Shorthand Dictionary, Claim Check Integration
- Systemic Guardrails – Root Prompt construction, Context Injection, Fact Check List design, Semantic Filter, privacy protection techniques (data masking, pseudonymisation, generalisation)
Applied Prompt Systems and Integration
- Game Play Pattern and Scenario Simulation – Current State setup, Available Actions, CoT-driven outcome simulation, surfacing unexpected consequences
- Tail Generation and Multi-Turn Prompt Strategy – Tail Generation for persistent output formatting, Focus Footer, session continuity and multi-turn chaining, Prompt Chaining workflow design
- Applied Integrated Prompt Engineering Session – assembly of complete prompt system (Root Prompt + Context Injection + Template + Claim Check + Cognitive Verifier + Semantic Filter + Game Play entry point), peer review, assessment preparation and briefing
Assessment Methods
- Case Study Written Assessment
- Individual Project Presentation
Trainers
Meet Your Educators
Trainer Bio
Tat Yuen
Tat Yuen is an ACLP-certified trainer and lifelong learner with over 30 years of experience in enterprise technology, spanning early systems through to modern cloud platforms. He developed the Prompt Engineering course framework and the Divergent-Convergent-Assessment methodology that structures this course. He now focuses on helping learners understand and use AI in practical, everyday contexts. Drawing on decades of experience translating complex ideas for diverse audiences, he teaches coding, AI literacy, and technology adoption with clarity and relevance. His goal is to empower learners to engage thoughtfully and confidently with AI in their daily work and lives.
Course Fee
Fund Your Brain Gain
Gain practical skills and resources you can apply immediately at work.
Course Fee
S$299.00S$499.00
Course Schedule
Mark Your Calendar!
Duration: : 1 Day (8 Hours)
Discover course schedules crafted with you in mind, structured for balance, driven by your goals, ready for action.
| Learning Mode | Course Dates | Duration | Trainer |
|---|---|---|---|
| In-Person | 14 Sep 2026 (Mon) | 9:00am - 6:00pm | |
| In-Person | 20 Oct 2026 (Tue) | 9:00am - 6:00pm | |
| In-Person | 17 Dec 2026 (Thu) | 9:00am - 6:00pm |
Click on the course dates above to register online.
Frequently Asked Questions (FAQs)
The Need-to-Know Stuff, Fast
Everything you need to know about the course. Can’t find the answer you’re looking for? Please contact our friendly team.
This course is designed for business professionals, policy practitioners, strategists, consultants, and digital team members who want to move beyond basic AI prompting toward systematic, engineered prompt design for complex, multi-stakeholder challenges.
Basic familiarity with at least one AI tool such as ChatGPT, Gemini, or Claude is required. Learners who have used AI for everyday tasks will be well-positioned to engage. The course does not require technical or coding expertise. Learners who have completed the Generative AI course will find this a natural and well-prepared next step.
Each learner or group selects one wicked problem from the four problem briefs in the course handout. A wicked problem is one with no single correct solution, competing stakeholder values, and genuine uncertainty. Working on a real, complex problem for the full day is what makes the learning durable.
The course covers more than 10 prompt patterns across the three phases: Persona Pattern, Flipped Interaction, Cognitive Verifier, Alternate Approach, Outline Expansion, Meta Language Creation, Chain-of-Thought, Zero-Shot CoT, ReAct Prompting, Task Decomposition, LLM Grading, Template Pattern, Root Prompt, Context Injection, Semantic Filter, Game Play Pattern, and Tail Generation.
The assessment has two parts. A1 is a 45-minute open-book written assessment with two sections: three AI failure scenario questions (Part 1, 15 min) and one fully engineered prompt design from a new scenario (Part 2, 25 min). A2 is a 15-minute individual oral presentation covering the Tier 3 reflection questions and a brief debrief on the prompt system built during the day.
A Certificate of Completion by Equinet Academy will be awarded to candidates who demonstrate competency across both assessment components and achieve at least 75% attendance.
Brochure Download
See What’s Inside: Get the Brochure
Explore the course outline, key topics, and learning outcomes you will gain from this training.
Related Courses
More to explore!
Take your learning further, discover handpicked courses designed to expand your skills and spark new ideas.
Create, Collaborate, and Boost Productivity with GenAI Tools
Leverage AI Tools to Power Smarter, Faster Marketing Strategies
Transform Your Organisation’s Digital Capabilities Through AI Empowerment
