Instructor-led training

AI Prompting-Copilot

AI training from Hartmann Software Group, delivered onsite or live online.

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What you'll learn

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.

Prerequisites

Basic computer skills.

Course Outline

Module 1: Understanding AI GPT Based Tools

Lecture Outline

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

Lecture Outline

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

Lecture Outline

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

Lecture Outline

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.

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