Weekend batches
October 2026
09:00–15:00 · flexible hours
$799per learner
24 Oct 2026
4 of 25 seats available
31 Oct 2026
10 of 25 seats available
Generative AI
Generative AI Fundamentals in Pitcairn
Generative AI Fundamentals training in Pitcairn provides a practical introduction to Generative AI, Large Language Models (LLMs), prompt engineering, AI tools, and real-world applications. Learn core concepts including tokens, embeddings, RAG, AI agents, responsible AI, and effective prompting through structured learning and practical examples. This course helps beginners, professionals, and AI enthusiasts build essential Generative AI skills for modern technology and business applications
40 Hours/5 daysFoundation4.5 · 1,000 reviews
Training calendar
Upcoming four-month course schedule
A new weekday and weekend batch starts every week. Each batch runs for 5 training days, based on the course duration of 40 Hours/5 days.
Weekend batches
November 2026
09:00–15:00 · flexible hours
$799per learner
07 Nov 2026
25 of 25 seats available
14 Nov 2026
25 of 25 seats available
21 Nov 2026
25 of 25 seats available
28 Nov 2026
25 of 25 seats available
Weekend batches
December 2026
09:00–15:00 · flexible hours
$799per learner
05 Dec 2026
25 of 25 seats available
12 Dec 2026
25 of 25 seats available
19 Dec 2026
25 of 25 seats available
26 Dec 2026
25 of 25 seats available
Weekend batches
January 2027
09:00–15:00 · flexible hours
$799per learner
02 Jan 2027
25 of 25 seats available
09 Jan 2027
25 of 25 seats available
16 Jan 2027
25 of 25 seats available
23 Jan 2027
25 of 25 seats available
30 Jan 2027
25 of 25 seats available
Course curriculum
What you’ll learn
40 Hours/5 daysFoundation
- Module 1: Introduction to Generative AI
- Understanding Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI.
- Evolution and growth of Generative AI technologies.
- Key characteristics and capabilities of Generative AI systems.
- Common Generative AI applications across industries in Pitcairn.
- Module 2: Generative AI Models and Technologies
- Introduction to Large Language Models (LLMs) and foundation models.
- Understanding tokens, context windows, parameters, and model training.
- Overview of text, image, audio, and multimodal Generative AI models.
- Understanding how AI models generate and transform content.
- Module 3: Large Language Models (LLMs)
- Fundamentals of LLM architecture and language processing.
- Introduction to transformers and attention mechanisms.
- Understanding pre-training, fine-tuning, and inference.
- Exploring common LLM use cases for professionals in Pitcairn.
- Module 4: Prompt Engineering Fundamentals
- Understanding prompts and effective prompt structures.
- Writing clear instructions and providing relevant context.
- Exploring zero-shot, few-shot, and role-based prompting.
- Improving AI responses through iterative prompt refinement.
- Module 5: Working with Generative AI Tools
- Introduction to ChatGPT, Google Gemini, and Microsoft Copilot.
- Creating and refining text using AI assistants.
- Summarizing, rewriting, brainstorming, and analyzing information.
- Applying AI tools to workplace productivity and professional tasks in Pitcairn.
- Module 6: Multimodal Generative AI
- Understanding multimodal AI and its capabilities.
- Working with text and image-based AI interactions.
- Exploring AI-assisted image generation and visual analysis.
- Understanding practical multimodal AI applications.
- Module 7: Generative AI for Productivity and Business
- Using Generative AI for content creation and communication.
- Applying AI to research, documentation, and knowledge work.
- Exploring AI use cases in marketing, education, customer service, and operations.
- Identifying opportunities for AI-assisted workflows in Pitcairn.
- Module 8: Responsible and Ethical AI
- Understanding responsible Generative AI practices.
- Exploring AI bias, misinformation, hallucinations, and reliability.
- Understanding privacy, security, copyright, and data protection considerations.
- Developing responsible approaches to using AI-generated content.
- Module 9: Practical Generative AI Exercises
- Creating effective prompts for real-world scenarios.
- Performing content generation, summarization, and information analysis tasks.
- Comparing AI-generated responses and improving output quality.
- Applying Generative AI techniques to practical workplace scenarios in Pitcairn.
- Module 10: Generative AI Applications and Future Trends
- Exploring emerging Generative AI applications and technologies.
- Understanding AI agents, RAG, and AI-powered automation at a foundational level.
- Identifying opportunities for continued Generative AI learning.
- Understanding career and professional development opportunities in Generative AI in Pitcairn.
Entry requirements and prerequisites
- Basic Computer Skills – Familiarity with computers, web browsers, file management, and commonly used digital applications is recommended.
- Basic Internet Knowledge – Participants should understand basic concepts such as websites, search engines, online platforms, and cloud-based applications.
- No Prior AI Experience Required – This course is designed for beginners and does not require previous experience in Artificial Intelligence, Machine Learning, or Generative AI.
- Basic Understanding of Technology – A general awareness of software applications, digital tools, or modern technology can help learners understand Generative AI concepts more easily.
- Willingness to Learn – An interest in Artificial Intelligence, ChatGPT, AI-powered tools, automation, and emerging technologies will help participants get the most from the training.
- Optional Programming Knowledge – Basic Python or programming knowledge can be helpful for learners interested in exploring technical Generative AI applications, but it is not mandatory.
Career value
Reasons to choose this course
Build Generative AI Foundations
Learn essential Generative AI concepts, terminology, and applications through structured training in Pitcairn.
Master Prompt Engineering Basics
Develop practical skills for creating clear and effective prompts for AI tools and applications in Pitcairn.
Explore Leading AI Tools
Gain hands-on exposure to ChatGPT, Google Gemini, and Microsoft Copilot for practical workplace use.
Learn Through Practical Exercises
Apply Generative AI concepts to content creation, research, productivity, and real-world business scenarios.
Develop Job-Ready AI Skills
Build foundational capabilities that support modern AI-related roles and career opportunities in Pitcairn.
Learn from Experienced Trainers
Gain guidance from instructors who help simplify complex Generative AI concepts and practical workflows.
Advantages
Turn learning into practical capability
Understand How Generative Models Work
Explore the fundamentals of generative models, LLMs, tokens, context, and AI-generated outputs to develop stronger technical awareness in Pitcairn.
Improve AI-Assisted Productivity
Learn how Generative AI can support everyday activities such as drafting, summarization, brainstorming, information organization, and knowledge work.
Develop AI Communication Skills
Understand how to communicate effectively with AI systems by providing relevant context, instructions, examples, and desired output formats.
Explore Multimodal AI Capabilities
Discover how modern Generative AI can work with different types of information, including text, images, and other digital content.
Connect AI Concepts with Business Use Cases
Examine practical applications of Generative AI across areas such as marketing, education, customer service, software development, and business operations in Pitcairn.
Create a Foundation for Advanced AI Learning
Establish the knowledge needed to progress toward specialized areas such as LLMs, RAG, AI agents, Copilot development, and enterprise Generative AI.
Exam format & assessment
- Exam Level: Beginner – Generative AI Fundamentals
- Exam Type: Online objective and practical assessment covering foundational Generative AI concepts and hands-on AI tasks in Pitcairn.
- Number of Questions: 40 questions
- Exam Duration: 90 minutes
- Passing Score: 70% (28 out of 40)
- Question Format: Multiple-choice questions covering Generative AI, LLMs, prompting, multimodal AI, AI tools, and responsible AI.
- Practical Assessment: Candidates may complete practical tasks involving prompt creation, content generation, summarization, analysis, and AI-assisted productivity.
- Key Exam Topics: Generative AI fundamentals, LLM basics, prompt engineering, ChatGPT, Gemini, Copilot, multimodal AI, AI applications, and responsible AI.
- Prerequisites: No formal prerequisites. Basic familiarity with computers and web-based applications is helpful.
- Assessment Platform: Online assessment through the StepMerit Learning platform.
- Certification: Successful candidates receive a StepMerit Course Completion Certificate.
- Exam Preparation: Practical exercises, instructor demonstrations, and course assignments help learners prepare for the assessment in Pitcairn.
Bring your learning together
Course conclusion
Generative AI Fundamentals provides a practical foundation for understanding and applying modern Generative AI technologies. Throughout the course, learners explore Generative AI, Large Language Models (LLMs), prompt engineering, multimodal AI, popular AI tools, practical use cases, and responsible AI practices. The training helps participants develop essential skills for using AI effectively in professional, academic, and business environments. Learners in Pitcairn can apply these concepts to content creation, research, productivity, communication, automation, and AI-assisted workflows. With hands-on exercises and real-world scenarios, the course prepares beginners to confidently explore the rapidly evolving Generative AI ecosystem and progress toward advanced areas such as RAG, AI agents, LLM application development, and enterprise AI. Completing this course can provide a strong starting point for learners seeking to develop relevant AI skills and pursue further opportunities in the growing Generative AI field.
Target audience
- Students and Graduates
- Working Professionals
- Business and Management Professionals
- IT and Technology Professionals
- Marketing and Content Professionals
- Entrepreneurs and Business Owners
- Educators and Trainers
- AI Enthusiasts and Career Switchers
- Researchers and Knowledge Workers
- Aspiring AI Professionals
Verified learning evidence
Expertise, recognition and learner outcomes
Course-specific expertise
- Trainer Experience: 5+ years of professional and training experience in AI, Machine Learning, Generative AI, and related technologies.
- Course Training Experience: Delivered 150+ hours of training focused on Generative AI fundamentals, LLMs, prompt engineering, AI tools, and practical business applications.
- Candidates Trained: 250+ candidates including students, working professionals, developers, business professionals, educators, and AI enthusiasts.
- Training Delivery: Conducted instructor-led online and classroom training with interactive demonstrations, practical exercises, assignments, and real-world use cases.
- Generative AI Expertise: Practical experience with Generative AI concepts, LLMs, prompt engineering, AI-assisted content creation, summarisation, analysis, and productivity workflows.
- AI Tools Exposure: Hands-on training experience with tools such as ChatGPT, Google Gemini, Microsoft Copilot, and other Generative AI platforms.
- Educational Use Cases: Guided learners in implementing Generative AI for lesson planning, content creation, assessments, research assistance, student support, and educator productivity.
- Hands-On Learning: Delivered practical exercises that enable learners to create prompts, evaluate AI outputs, refine responses, and build AI-assisted workflows.
- Project Guidance: Supported candidates in developing practical Generative AI projects based on educational, business, productivity, and content-generation scenarios.
- Assessment Experience: Evaluated learners through practical assignments, scenario-based activities, quizzes, demonstrations, and project-based assessments.
Documented learner outcomes
- Problem Statement – An educational organisation manages large volumes of course materials, lesson plans, study resources, student queries, assessments, and institutional information. Students and educators may spend considerable time searching for relevant information, creating learning content, preparing assessments, and responding to repetitive academic questions. The organisation needs a practical Generative AI solution to improve learning support, content creation, and educator productivity while maintaining accuracy, privacy, and responsible AI practices.
- How We Overcame the Problem – Learners identified suitable Generative AI use cases and designed AI-assisted workflows using tools such as ChatGPT, Gemini, and Microsoft Copilot. They developed effective prompts, structured context, applied appropriate prompting techniques, reviewed AI-generated outputs, and introduced human validation to reduce inaccurate or unsuitable responses.
- Overall Implementation – Learners implemented an educational AI workflow covering requirement identification → prompt design → content generation → response evaluation → refinement → human review → final delivery. The implementation demonstrated how Generative AI can support lesson planning, course-content creation, quiz generation, summarisation, personalised learning assistance, and academic communication.
- Learning Content Generation – Learners used Generative AI to create lesson summaries, explanations, examples, learning activities, study guides, and educational content based on defined learning objectives.
- Assessment Creation – Learners developed prompts for generating multiple-choice questions, scenario-based questions, quizzes, answer explanations, and practice assessments aligned with specified topics and difficulty levels.
- Student Support Implementation – Learners designed an AI-assisted academic support workflow capable of responding to common student questions, explaining concepts, summarising learning materials, and providing structured study guidance.
- Educator Productivity – Learners demonstrated how Generative AI can assist educators with lesson planning, classroom activities, assignment ideas, feedback drafts, presentations, and other repetitive content-development tasks.
- Prompt Engineering Evidence – Learners demonstrated the ability to create clear prompts using role, context, instructions, constraints, examples, and expected output formats to improve the quality and consistency of AI-generated responses.
- AI Output Evaluation – Learners evaluated generated content for accuracy, relevance, completeness, clarity, bias, and potential hallucinations before using it in an educational context.
- Responsible AI Implementation – Learners identified the importance of human oversight, privacy protection, responsible use of student information, intellectual-property considerations, bias awareness, and verification of AI-generated educational content.
- Practical Evidence of Learning – Learners produced sample prompts, AI-generated educational resources, lesson plans, quizzes, summaries, student-support responses, evaluation checklists, and refined outputs as evidence of their practical Generative AI skills.
- Overall Learner Outcome – By completing the implementation, learners demonstrated the ability to identify suitable Generative AI opportunities, design effective prompts, use AI tools productively, evaluate AI-generated content, and implement responsible AI workflows for educational organisations.
Helpful answers for planning your training
Frequently asked questions
Generative AI Fundamentals is a beginner-level course covering Generative AI, LLMs, prompting, AI tools, multimodal AI, and practical applications in Pitcairn.
The course is suitable for students, graduates, working professionals, business users, IT professionals, educators, entrepreneurs, and AI enthusiasts in Pitcairn.
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