HARTMANN software group

AI Prompting-Copilot Training in Whittier

Enroll in or hire us to teach our AI Prompting-Copilot class in Whittier, California by calling us @303.377.6176. Like all HSG classes, AI Prompting-Copilot may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, AI Prompting-Copilot may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.
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Course Description

  

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.

Course Length: 1 Days
Course Tuition: $150 (US)

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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