Course Outline
Applied Artificial Intelligence Workshop
Structure: Intensive, hands-on training session
Objective: Cultivate operational proficiency in leading AI frameworks and practical government workflows.
Module 1 – AI Platform Capabilities and Comparative Analysis (75 minutes)
This section provides an overview of the fundamental features of premier AI platforms utilized in contemporary professional and public sector environments.
Platforms analyzed:
• ChatGPT
• Google Gemini
• Microsoft Copilot
Subject matter covered:
• Document processing protocols and file constraints
• Context window dynamics
• Data management standards and privacy implications
• Functional tools and system integrations
• Process automation and workflow capabilities
• Advantages and constraints of each platform
Instructor-led Demonstration
The instructor illustrates key features and differentiates between platforms by demonstrating real-world application scenarios relevant to government operations.
Independent Practical Exercise
Participants construct a personalized AI tool comparison matrix using a provided standardized template.
Assessment criteria:
• Usability and user experience
• Quality and accuracy of generated content
• Information retrieval and research capacity
• Potential for workflow integration
Learning outcomes for this module:
• Distinguish between major AI platforms and their respective capabilities
• Identify specific strengths and limitations of each tool
• Select the appropriate AI tool for diverse professional tasks
Module 2 – Research and Multimodal Laboratory (90 minutes)
This segment focuses on the application of AI for research synthesis, information consolidation, and critical evaluation of data.
Exercise 1 Long-Form Document Reasoning
Independent Practical Exercise
Participants process a multi-document dataset to evaluate cross-platform analytical performance.
Assessment criteria:
• Citation integrity
• Logical coherence
• Depth of synthesis
• Summarization of complex material
Exercise 2 Hallucination Detection Protocol
Independent Practical Exercise
Participant actions:
• Query obscure factual information
• Manually verify cited sources
• Compare response consistency across tools
Group discussion addresses techniques for identifying and mitigating potential hallucinations.
Exercise 3 Voice Interaction Laboratory
Guided Practical Exercise
Participant actions:
• Utilize voice interaction for ideation
• Interrupt AI responses during generation
• Request revisions through conversational prompts
Debrief discussion topics:
• Scenarios where voice interaction outperforms text input
• Situations where structured prompting yields superior results
Learning outcomes for this module:
• Assess AI responses for accuracy and reliability
• Utilize AI tools for research and document analysis
• Apply verification techniques to detect erroneous information
Module 3 – Custom AI Assistant Development (2 hours)
Participants examine how modern AI frameworks enable the creation of custom assistants tailored to specific operational needs.
Platforms introduced:
• ChatGPT Custom GPTs
• Google Gemini Gems
• Microsoft Copilot Agents
Instructor-led Demonstration
The instructor demonstrates the process of building a custom AI assistant, covering:
• Defining objective and scope
• Drafting instructions and behavioral guidelines
• Uploading reference documents
• Testing and refining response quality
Independent Practical Exercise
Participants select one platform to develop a custom AI assistant.
Participant actions:
• Define the assistant’s intended purpose
• Create instructions and system guidance
• Upload supporting documents or knowledge bases
• Test behavior and refine outputs
Instructor provides active facilitation and technical troubleshooting.
Learning outcomes for this module:
• Design custom AI assistants for specific workflows
• Configure instructions and knowledge sources
• Deploy assistants that automate routine knowledge tasks
Module 4 – Workflow Automation Laboratory (90 minutes)
Participants investigate how AI can optimize real-world work processes through integration with productivity software.
Participants select one of two workflow tracks.
Track A – Microsoft Ecosystem
Participant tasks using Microsoft tools:
• Analyze Excel datasets
• Generate insights and summaries
• Convert findings into PowerPoint presentations
Track B – Google Ecosystem
Participant tasks using Google tools:
• Analyze documents stored in Google Drive
• Generate structured reports
• Create summarized outputs for email communication
Comprehensive facilitator support is provided during the exercises.
Learning outcomes for this module:
• Utilize AI to accelerate analysis workflows
• Integrate AI outputs with productivity tools
• Design simple automation pipelines for common tasks
Module 5 – Advanced Data Analysis with AI (75 minutes)
Participants explore how AI tools facilitate data exploration, code generation, and visualization.
Python and Data Visualization Exercise
Independent Practical Exercise
Participant tasks using AI tools:
• Perform exploratory data analysis
• Generate charts and visualizations
• Refine insights through iterative prompting
Platform comparison criteria:
• Code transparency
• Error correction and debugging capabilities
• Clarity of visualizations
Discussion focuses on how non-technical staff can leverage AI-assisted data analysis.
Learning outcomes for this module:
• Use AI tools to analyze datasets
• Generate visualizations and insights
• Iterate on data analysis using AI-assisted workflows
Requirements
Required understanding of:
• Basic workplace productivity tasks such as research, writing, document creation, and information analysis
• How AI assistants can support common professional workflows
Experience with:
• Using AI tools such as ChatGPT, Google Gemini, and Microsoft Copilot for basic prompting or task assistance
Programming experience:
• No programming experience is required
Participants should have access to at least one AI platform (free versions are sufficient).
Audience
• Analysts
• Researchers
• Business professionals
• AI champions exploring practical AI workflows