Generative AI Intermediate Prompting Training in Ireland

This course moves beyond a basic introduction to generative AI and focuses on using these technologies more effectively and responsibly in professional environments. Participants explore how modern generative AI systems create content, support decision-making, and enable new approaches to work across organisations.

The programme examines how generative AI differs from traditional AI, the different types of content these systems can produce, and the opportunities they can create for businesses. It also considers important limitations and risks, including hallucinations, bias, information security, transparency, and governance.

A key part of the course focuses on prompt design. Learners develop techniques for creating clearer, more contextual, and structured prompts that can produce more useful AI outputs. The course also introduces Retrieval Augmented Generation (RAG), exploring how trusted or organisational information can be combined with generative AI to improve the relevance and potential accuracy of responses.

Through examples, discussions, and practical interaction with generative AI tools, participants have opportunities to relate these techniques to their own roles, business processes, and organisational requirements.

This course is designed to be accessible without requiring deep technical expertise. Participants should have:

• A general awareness of digital technologies used within modern organisations,
• An interest in how AI is changing business processes, professional roles, and decision-making.

Previous experience in AI development, data science, or programming is not required.

This course is particularly suitable for:

• Beginner and intermediate learners developing their knowledge of generative AI,
• Professionals who want to use AI tools more effectively at work,
• People working across Business Operations, Marketing, HR, Education, Healthcare, and Technology,
• Managers and team leaders who may guide AI usage within their teams,
• Professionals involved in AI adoption decisions,
• Organisations seeking to establish a shared understanding of generative AI across different functions.

By the end of this course, participants will be able to:

• Describe the main capabilities of generative AI,
• Explain how generative AI can produce content including text, images, audio, video, and code,
• Distinguish between Traditional AI and Generative AI approaches,
• Evaluate key strengths and limitations of generative AI,
• Identify important risks including hallucinations, bias, security, and governance concerns,
• Assess real-world generative AI use cases across different industries,
• Consider whether a business problem is appropriate for a generative AI solution,
• Apply Structured Prompting techniques to improve AI interactions,
• Define context, instructions, and output requirements more effectively,
• Explain the basic principles of Retrieval Augmented Generation (RAG),
• Evaluate how RAG can support greater relevance and potential accuracy in organisational AI solutions.

Module 1 – Introduction to Generative AI

This module establishes a common foundation for understanding generative AI and its growing range of applications.

• Overview of Artificial Intelligence
• Evolution of AI technologies
• Traditional AI and Generative AI
• Core characteristics of generative models
• Text generation
• Image generation
• Audio and music generation
• Video generation
• Code generation
• Organisational applications of generative AI
• Examples and demonstrations of commonly used Generative AI tools

Module 2 – Strengths and Challenges of Generative AI

This module examines the potential advantages of generative AI alongside the limitations that organisations need to consider.

• Potential productivity benefits
• Creativity and idea generation
• Scalable content creation
• Supporting access to information and business processes
• Hallucinations and inaccurate outputs
• Bias and potentially unbalanced results
• Data dependency
• Transparency challenges
• Privacy and information security
• Intellectual property and legal considerations
• Ethical use of AI
• Risks of overreliance
• Importance of AI Governance
• The role of human oversight

Module 3 – Real-World Applications and Generative AI Implementation

This module considers how generative AI can be applied to practical business challenges.

• Marketing use cases
• Healthcare examples
• Education applications
• Customer Service
• Technology and software processes
• Content creation and knowledge management
• Identifying suitable business problems for Generative AI
• Evaluating potential value
• Technical and organisational feasibility
• Organisational readiness
• Leadership considerations
• Employee and user impact
• Customer considerations
• Regulatory requirements
• Evaluating strategic options for AI adoption

Module 4 – Effective Prompt Design

This module develops practical techniques for communicating more effectively with generative AI systems.

• What is a prompt?
• How prompt design influences AI output
• Writing clear and specific instructions
• Providing relevant context
• Defining expected outputs
• Creating Structured Prompts
• Breaking complex tasks into manageable steps
• Prompting for business and professional scenarios
• Evaluating AI-generated outputs
• Improving prompts based on results
• Iterative Prompting
• Developing more consistent and useful outputs

Module 5 – Retrieval Augmented Generation (RAG)

This module introduces the use of external or organisational information to support generative AI systems.

• Introduction to Retrieval Augmented Generation
• Basic principles of how RAG works
• Supporting a Generative AI model with relevant information
• The role of internal organisational data
• Providing context from trusted information sources
• Improving response relevance
• Potential improvements to accuracy
• Comparing RAG with standard prompting
• Organisational knowledge base use cases
• Evaluating the business value of RAG

Hands-On Learning

Throughout the programme, participants interact with generative AI tools and experiment with different prompting approaches.

Practical activities may include:

• Testing the same task with different prompts,
• Improving unclear or incomplete prompts,
• Exploring the effect of adding context and instructions,
• Applying Structured Prompting techniques,
• Evaluating AI outputs for quality and usefulness,
• Developing prompts for realistic workplace scenarios,
• Exploring RAG through example business use cases.

This practical approach helps participants move beyond simply understanding generative AI and towards using it more effectively, thoughtfully, and responsibly within professional environments.