Prompt Engineering and Practical Generative AI for Professionals

The programme

Programme overview

Generative AI tools are now embedded in everyday workplace tasks, but most professionals use them inefficiently, without a structured approach. This Prompt Engineering and Practical Generative AI for Professionals course builds practical, transferable prompt engineering skills — moving participants beyond casual AI use to structured, reliable workflows across research, writing, analysis, and decision support, using tools such as ChatGPT, Claude, and Gemini.

AI tools have moved beyond one-off chat interactions: today’s platforms increasingly use memory, tools, and multi-step agentic features, which is why leading AI labs now describe context engineering as the natural progression of prompt engineering. This Prompt Engineering and Practical Generative AI for Professionals course reflects that shift directly, alongside the core skills of structured prompting, critical output evaluation, and responsible use — the baseline AI fluency industry sources now compare to spreadsheet literacy.

Who should attend

Who is this programme for?

This Prompt Engineering and Practical Generative AI for Professionals course is designed for:

• Professionals Across Any Department Who Use or Want to Use Generative AI Tools in Their Daily Work
• Managers and Team Leads Seeking to Embed AI-Assisted Workflows into Their Teams
• Marketing, HR, Finance, and Operations Staff Wanting Practical Productivity Gains
• Anyone Who Has Used AI Tools Casually and Wants to Use Them with Genuine Skill

Learning outcomes

What you will learn

This Prompt Engineering and Practical Generative AI for Professionals course equips you to use generative AI tools with genuine skill — producing reliable, usable output and applying sound judgement to what the AI gives back.

By the end of this Prompt Engineering and Practical Generative AI for Professionals course, you’ll be able to:

• Design clear, structured prompts that reliably produce accurate, usable outputs
• Understand how modern reasoning-capable AI models plan and respond, and what that means for how you prompt them
• Apply prompt engineering techniques across research, writing, analysis, and reporting tasks
• Apply context engineering principles to get reliable results from AI tools that use memory, tools, or multi-step agentic features
• Evaluate AI outputs critically and recognise their limitations and failure modes
• Apply data privacy and responsible-use principles when working with AI tools
• Build repeatable AI-assisted workflows suited to your own role and department
• Build a personal AI workflow toolkit you can apply and keep refining after the course
• A Baseline Skill, Not a Specialist One: Build the prompt engineering fluency industry sources now compare to spreadsheet literacy — essential, not niche.
• Beyond One-Off Chat: Learn context engineering, the skill now needed to get reliable results from AI tools that use memory, tools, and multi-step agentic features.
• Judgement, Not Just Output: Evaluate AI outputs critically, so you catch errors and limitations before they become your problem.
• Real Tools, Real Practice: Work hands-on with ChatGPT, Claude, and Gemini throughout, not screenshots and theory.
• A Toolkit You’ll Keep Using: Leave with a personal AI workflow toolkit built around your own role and everyday tasks.

Explore the programme

Detailed programme outline

Module 1: Foundations of Prompt Engineering

• How large language models interpret and respond to instructions
• Why clarity consistently beats cleverness in prompt design
• How modern reasoning-capable models plan internally before responding, and what that means for you
• Common beginner mistakes that produce vague or unreliable outputs

Practical Exercise: Rewrite a vague, casual prompt into a clear, structured instruction and compare the outputs.

Module 2: Structured Prompting Techniques

• Context-setting: giving the model the background it needs to respond well
• Role assignment and its effect on tone, depth, and output style
• Iterative refinement: treating a first response as a draft, not a final answer
• Building reusable prompt templates for repeated tasks

Practical Exercise: Build a reusable prompt template for a task you complete regularly in your role.

Module 3: Context Engineering and Working with AI Agents

• How context engineering differs from — and extends — classic prompt engineering
• Managing memory, tools, and multi-step tasks when working with agentic AI features
• Structuring instructions for tools that browse, search, or take multi-step actions on your behalf
• Avoiding the compounding errors that occur when early context is unclear

Practical Exercise: Structure a multi-step task for an AI tool with agentic features, and evaluate how it manages context across steps.

Module 4: Applying AI to Research, Writing, and Reporting

• Using AI tools to accelerate research without sacrificing accuracy
• Structuring prompts for first drafts, editing, and tone adjustment
• Building consistent, professional reports and summaries with AI assistance
• Maintaining your own voice and judgement in AI-assisted writing

Practical Exercise: Use AI assistance to research, draft, and refine a short report on a real work topic.

Module 5: Applying AI to Analysis and Decision Support

• Using AI tools to support data interpretation and summarisation tasks
• Structuring prompts for comparative analysis and decision support
• Combining AI output with your own domain expertise for better decisions
• Recognising where AI-assisted analysis needs human verification

Practical Exercise: Use AI assistance to summarise and interpret a sample dataset or business scenario.

Module 6: Evaluating AI Outputs Critically

• Recognising hallucination, bias, and other common AI failure modes
• Techniques for verifying AI-generated facts, figures, and claims
• Knowing when an AI output is good enough to use, and when it isn’t
• Building a habit of critical review into every AI-assisted task

Practical Exercise: Review a set of AI-generated outputs and identify errors, gaps, or unsupported claims.

Module 7: Responsible AI Use and Your Personal Workflow Toolkit

• Data privacy and confidentiality principles when using AI tools
• Responsible-use principles for sharing organisational information with AI platforms
• Building a personal AI-assisted workflow suited to your own role
• Setting a plan for continuing to develop your prompt and context engineering skills

Practical Exercise: Build a personal AI workflow toolkit covering your three most common tasks.

Assessment & Certification

Participants will complete a practical capstone project demonstrating their ability to apply the skills learned throughout the program. This may include a real-world business scenario, case study analysis, or AI-driven project implementation. Upon successful completion, participants will receive an Advanced Executive Assistant Masterclass Series 2 Certification.

Learning approach

How you will learn

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.– Course Material: Access well-crafted supporting resources.– Group Work: Collaborate on discussions and case studies for practical insights.– Workshops & Role-Play: Participate in immersive, scenario-based activities.– Practical Application: Focus on applying theoretical knowledge in real situations.– Post-Training Support: Receive extensive support after training for skill implementation.

Discuss your requirements

Dates, duration & arrangements

Contact Ratomac to confirm dates, duration, delivery format, entry requirements and any applicable assessment or certification before booking.