Upcoming Public Training Courses

Course overview: A deep hands-on workshop for developing practical AI proficiency across leading AI platforms and real business workflows. Participants compare tools, test AI outputs, build assistants, and use AI for analysis and reporting.

Module 1 — AI platform capabilities and comparison

• Compare ChatGPT, Google Gemini, and Microsoft Copilot.
• Explore file handling, document limits, context window behavior, and data privacy considerations.
• Review tools, integrations, automation capabilities, strengths, and limitations.
• Create an AI tool comparison matrix.
• Evaluate ease of use, output quality, research capability, and workflow integration potential.

Module 2 — Research and multimodal lab

• Use AI for research, synthesis, and critical evaluation of information.
• Upload multi-document materials and test analysis across platforms.
• Evaluate citation quality, logical consistency, depth of synthesis, and summarization ability.
• Run a hallucination detection drill using obscure factual questions and manual verification.
• Compare responses across tools and discuss mitigation techniques.
• Use voice interaction for brainstorming, revisions, and conversational iteration.

Module 3 — Custom AI assistants

• Explore how modern AI platforms support custom task-specific assistants.
• Compare ChatGPT Custom GPTs, Google Gemini Gems, and Microsoft Copilot Agents.
• Define assistant purpose, scope, instructions, and behavioral guidance.
• Upload knowledge documents and test responses.
• Build and refine a custom AI assistant with facilitator support.

Module 4 — Workflow automation lab

• Explore how AI can streamline real workplace processes.
• Microsoft track: analyze Excel datasets, generate insights, and convert findings into presentations.
• Google track: analyze Drive documents, generate structured reports, and create email-ready summaries.
• Design simple automation pipelines for recurring tasks.
• Integrate AI outputs with productivity tools.

Module 5 — Advanced data analysis with AI

• Use AI tools for dataset exploration, code generation, and visualization.
• Upload a dataset and perform exploratory data analysis.
• Generate charts and refine insights through iterative prompting.
• Compare platforms for code transparency, debugging support, and visualization clarity.
• Discuss how non-developers can use AI-assisted data analysis in practical work.

Course overview: A focused, practical comparison of ChatGPT, Gemini, and Microsoft Copilot. Participants compare the platforms side by side through workplace tasks and leave with a decision framework for choosing the best AI tool for different workflows.

Phase 1 — AI landscape and platforms

• Overview of the current AI ecosystem.
• Walkthrough of the main interfaces and user experience differences.
• Comparison of platform capabilities.
• Introduction to an AI Tool Decision Matrix.

Phase 2 — Comparative stress tests

• Research synthesis: comparing how each tool gathers, summarizes, and structures information.
• Spreadsheet and reasoning task: testing analytical and reasoning capabilities.
• Multimodal reasoning: comparing how platforms handle images, documents, and mixed inputs.
• Creative output comparison: evaluating quality, tone, consistency, and usefulness.
• Participants record results in the Decision Matrix.

Phase 3 — Workflow integration and custom AI

• Microsoft 365 automation demonstration.
• Google Workspace integration demonstration.
• Custom GPTs vs Copilot Agents vs Gemini Gems.
• Simple AI agent example shown by the trainer.

Phase 4 — Strategy, privacy, and ROI

• Data privacy considerations in workplace use.
• Free vs paid plans and practical limitations.
• Subscription ROI and tool selection strategy.
• Participants finalize their Decision Matrix.

Final decision matrix and Q&A

• Best tool for research.
• Best tool for writing.
• Best tool for productivity and everyday workflows.
• Instructor-led discussion and Q&A.

Course overview: A hands-on introduction to using Claude Code for people without a technical background. Participants learn how to build small projects, automate tasks, work with files, and connect Claude to everyday tools using plain English.

Phase 1 — Meet Claude Code

• What Claude is and how Claude Code differs from regular chat.
• Orientation in the Claude app and workspace.
• The describe → plan → act → review workflow.
• Understanding permissions and why Claude asks before creating files or running actions.
• First build: create and refine a simple styled webpage from a short instruction.

Phase 2 — Building real things with plain English

• Build an interactive dashboard with sample data, charts, and visual elements.
• Use Claude for data analysis: summarize a CSV, identify trends, find highs/lows, and generate a chart.
• Create a simple utility such as a converter, quiz app, or budget calculator.
• Review what Claude created behind the scenes: files, code, outputs, and structure.
• Collect effective prompts in a practical Prompt Playbook.

Phase 3 — Working smarter with Claude Code

• Prompting principles: specific vs vague instructions.
• Live comparison of weak and strong prompts.
• Iterating, refining, undoing, and asking Claude to explain design or code choices.
• Working with uploaded files, documents, and spreadsheets.
• Chaining requests into multi-step workflows.
• Understanding context windows, usage, subscriptions, and when to use Claude Code vs regular Claude chat.

Phase 4 — Connecting Claude to your tools with MCP

• What Model Context Protocol (MCP) is and why it matters.
• Using connectors to turn Claude into a connected workflow assistant.
• Browsing and adding integrations from the connectors directory.
• Example workflow: checking calendar meetings and drafting preparation emails.
• Guided exercise using a connected service such as Gmail, Google Drive, or Google Calendar.
• Permissions, OAuth, tool access, and security awareness.

Phase 5 — Capstone and wrap-up

• Build a polished landing page or portfolio site.
• Create a data analysis pipeline that produces a visual report.
• Build an interactive tool for a real workflow problem.
• Create a connected workflow using data from an external service.
• Review next steps, reference resources, prompt patterns, and useful integrations.

Course overview: A practical workshop on designing and building customized AI assistants for professional work. Participants explore Custom GPTs, Microsoft Copilot Agents, and Gemini Gems, then create a personal assistant and workflow blueprint.

Module 1 — Designing your AI assistant

• Introduction to customized AI assistants.
• Define the assistant’s purpose.
• Identify the workflows it should support.
• Decide what information sources it should use.

Module 2 — Building Custom GPTs

• Instructor demonstration of building a Custom GPT.
• Instruction design and behavioral configuration.
• Using knowledge uploads to support assistant responses.
• Participants begin building their own assistant.

Module 3 — Copilot Agents

• Overview of Microsoft Copilot Agents.
• Automation workflows and productivity integrations.
• Document generation pipelines.
• Practical examples for Microsoft 365 users.

Module 4 — Gemini Gems

• Demonstration of building Gemini assistants.
• Research synthesis use cases.
• Document summarization workflows.
• Structured analysis scenarios.

Module 5 — AI workflow automation

• Designing workflows such as research → summary → report.
• Turning meeting transcripts into summaries and action plans.
• Using AI to support data analysis and presentation generation.

Final exercise

• Complete an AI Workflow Blueprint.
• Map tasks to the right AI assistant.
• Choose the platform that best supports each workflow.