Opleiding: AI Prompting-Copilot (TOS-PRCO) [M-TOSPRCO]
OVERVIEW
This Copilot training course delivers a technically grounded introduction to GPT-based AI systems designed for enterprise and professional environments, with emphasis on model architecture concepts, generative AI capabilities, and real-world workflow integration. Participants explore how large language models process and generate text, examine the functional scope and constraints of Copilot Chat, and apply structured prompt-engineering frameworks to optimize output quality, relevance, and consistency. The curriculum extends beyond basic usage to address AI-assisted business communication, knowledge management, and customer service models, highlighting human-in-the-loop collaboration, quality control mechanisms, and decision-support use cases. A dedicated focus on AI ethics and bias mitigation equips learners with governance-oriented strategies, such as verification protocols, contextual prompting, and oversight practices, ensuring responsible deployment at scale. This course is designed to help technical and non-technical stakeholders alike evaluate, implement, and manage Copilot-enabled solutions with confidence, rigor, and operational impact.
Updated June 2026
OBJECTIVES
In this course participants will learn:
- A foundational understanding of AI and GPT-based tools such as Copilot
- Practical skills in writing clear, effective prompts to guide AI output
- Experience using Copilot for professional communication and collaboration
- The ability to evaluate AI-generated content for accuracy, clarity, and tone
- Awareness of ethical considerations and bias in AI-assisted work
- Confidence applying AI tools responsibly within everyday workflows
AUDIENCE
This course is designed to help technical and non-technical stakeholders alike evaluate, implement, and manage Copilot-enabled solutions with confidence, rigor, and operational impact
CERTIFICATION
NoneNEXT STEP
NoneCONTENT
Module 1: Understanding AI GPT Based Tools
- Introduction: AI in Contemporary Society
- Key definitions, historical evolution, and current applications across industries.
- Foundations of Generative AI and GPT Models
- Model architecture overview, training concepts, and limitations.
- Overview of AI Platform (Copilot ChatGPT)
- Analysis of capabilities, suitability for tasks, and real-world usage contexts.
- Integrating AI into Professional Workflows
- Practical considerations, productivity enhancement, and decision-support potential.
- Summary and Conceptual Takeaways
- Reinforcement of terminology and essential model characteristics.
Module 2: The Power of Prompt Engineering
- Introduction to Prompt Engineering Principles
- Why prompts matter; relationship between user intent and AI interpretation.
- Structure of Effective Prompts
- Role definition, constraints, tone guidance, and contextual detail.
- Analytical Review: Good vs. Poor Prompt Characteristics
- Theory-based examples (no live demonstration); analysis of clarity, specificity, and utility.
- Prompt Revision Framework
- Guidelines for systematic refinement to improve results.
- Lecture Summary and Preparation for Lab
- Overview of how learned principles will be applied.
Module 3: Copilot Chat for Business Communication Collaboration
- Copilot Chat Assistance for Team Workflows
- Meeting summaries, documentation support, and knowledge-base structuring.
- Copilot Chat in Customer Service Theory
- Ticket categorization, conversational tone guidelines, escalation logic.
- Human-AI Collaboration Model
- When to rely on AI, when humans intervene, and strategies for quality assurance.
- Lecture Review and Lab Preparation
- Outline of how theoretical concepts translate into simulated activities.
Module 4: Ethics Bias Awareness in Copilot Chat
- Introduction to AI Ethics for Copilot Chat
- Fairness, accountability, transparency frameworks, and privacy considerations.
- Understanding AI Bias related to use of Copilot Chat
- How training data, model design, and context influence biased results.
- Case Study Analysis (Conceptual Only)
- Review written examples of biased outputs; theoretical root-cause identification.
- Bias Mitigation Strategies
- Verification protocols, prompt techniques, and oversight mechanisms.
- Recap and Preparation for Lab
- How lab activities will reinforce ethical evaluation skills.
