Programme overview
Technical depth: Practitioner · Practical mode: Lab
Introduction
Employees across every function now have access to chat assistants and office copilots, yet most use them sporadically, paste in data they should not share, or trust answers they have not checked. Organisations gain little productivity and carry new information and quality risks. This Core Concept course builds the everyday competence to use generative AI safely and well at work, from understanding how the tools produce answers to verifying their output. Every day is hands-on with realistic work tasks, and participants leave with a Personal Generative AI Workflow Playbook for their own role.
Course Objectives
- Explain in plain terms how generative AI tools produce answers and why they can be wrong
- Apply the organisation's acceptable use rules to decide which tasks and data may be used with which tool
- Write structured prompts that produce usable drafts, summaries and analyses on the first or second attempt
- Use chat assistants and office copilots to draft documents, summarise meetings and analyse spreadsheet data
- Verify AI output for accuracy, bias, confidentiality and attribution before it is used or shared
- Assemble a Personal Generative AI Workflow Playbook for recurring tasks in the participant's role
Target Audience
- Administrative and coordination staff who draft correspondence, minutes and routine reports
- Analysts and officers who research, summarise and present information to managers
- Customer-facing staff who answer enquiries and prepare written responses
- Project and team support staff who track actions, schedules and status updates
- Specialists in HR, finance, procurement and operations who prepare documents in their field
Course Outline
Day 1: How Generative AI Works and Where It Fits
- How Large Language Models Work: Tokens, Training Data and Probabilistic Output
- AI, Machine Learning and Generative AI Terms Using ISO/IEC 22989 Definitions
- Generative AI Tool Landscape: Chat Assistants, Office Copilots and Image Generators
- Personal Task Audit: Mapping Daily Work Suited to Generative AI
- Acceptable Use Policy Review: Approved Tools, Data Classes and Human Review Points
Day 2: Prompting Methods and Output Evaluation
- Role, Task, Context and Format Prompt Pattern
- Few-Shot Examples and Step-by-Step Instructions
- Grounding Answers in Own Documents with File Upload and Retrieval Features
- Output Evaluation Rubric: Accuracy, Relevance, Completeness and Tone
- NIST AI 600-1 Generative AI Risk Categories for Everyday Users
Day 3: Generative AI in Everyday Work Tasks
- Email and Report Drafting Lab with a Chat Assistant
- Meeting Summaries and Action Logs with an Office Copilot
- Spreadsheet Analysis Lab: Formula Help, Pivot Summaries and Chart Suggestions
- Presentation Outline and Slide Drafting from a Source Document
- Multilingual Drafting and Translation Review with Back-Translation Checks
Day 4: Accuracy, Security and Responsible Use
- Hallucination Detection Using Source Checking and Lateral Reading
- Confidential Data Handling with Sensitivity Labels and Redaction
- Prompt Injection and Malicious Content Awareness from the OWASP Top 10 for LLM Applications
- Copyright, Attribution and Disclosure of AI-Assisted Work
- Bias Recognition in Generated Text and Images
Day 5: Workplace Scenarios and the Workflow Playbook
- Customer Enquiry Scenario Lab: From AI Draft to Approved Response
- Policy Summary Scenario Lab: Checking a Long-Document Summary Against the Source
- Personal Prompt Library Build for an Own Role
- Verification Checklist and Workflow Playbook Assembly
- Peer Demonstration and Time-Saving Estimate
Skills You Will Gain
- Generative AI Literacy
- Structured Prompt Writing
- AI-Assisted Document Drafting
- AI Output Verification
- Information Security Awareness
- Responsible AI Use
- AI-Assisted Data Summarisation
Why Attend This Course
- Return to work with a Personal Generative AI Workflow Playbook for the tasks you repeat every week
- Save time on drafting, summarising and analysis without handing over judgement to the tool
- Know which information you must never put into an AI tool, and why
- Compare practical uses with professionals from other sectors and countries doing similar work
Conclusion
Generative AI raises productivity only when employees understand what the tools can and cannot do and check what they produce. This course moves from how large language models generate answers, through prompting and evaluation methods, to hands-on use in everyday tasks and the accuracy, security and attribution risks that come with them. The final day turns that practice into a Personal Generative AI Workflow Playbook that participants take back to their role and apply from the first day back at work.