Course Outline
Introduction
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The concept of business automation using ChatGPT
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Models, agents, tools, and workflow structures
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Fixed workflows compared to agentic processes
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Selecting the appropriate level of autonomy
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The importance of human oversight and decision-making
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Moving beyond single conversations: reusable workflows and automated execution
Understanding Context and Memory
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Difference between chat context and persistent memory
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Storing process state explicitly
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Retaining decisions and key information across runs
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Limitations of context and potential loss of earlier data
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Continuing work across multiple sessions
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Distinguishing between permanent rules and current status
Organizing Work in ChatGPT
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Risks of mixing excessive tasks and changing requirements in one thread
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Identifying missing decisions and inconsistent outputs
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Breaking work down into smaller, well-defined tasks
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Transferring goals, sources, decisions, and results between tasks
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Determining when to use separate conversations or workflows
Working with Plugins, Projects, and Spaces
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Purposes of plugins, projects, and Spaces
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Organizing reusable instructions and tools
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Connecting workflows to business data sources
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Managing related conversations and shared materials
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Sharing documentation and results
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Setting up the environment for a specific business process
Building a Workflow with Memory
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Defining the workflow's goal
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Identifying inputs, steps, outputs, and acceptance criteria
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Recording process state between executions
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Applying previous decisions in subsequent runs
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Managing errors and incomplete executions
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Resuming a workflow after interruption
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Creating a prototype workflow for a specific task
Scheduling and Repeatable Execution
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Manual versus recurring workflow execution
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Setting execution frequency and time zone
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Notification and stopping rules
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Handling scenarios with no new data available
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Re-running workflows with saved state
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Comparing multiple executions
Evaluating Workflow Quality and Repeatability
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Setting quality criteria
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Verifying required fields, figures, sources, and output format
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Detecting duplicate or inconsistent results
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Differentiating acceptable wording variations from process errors
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Understanding variability in AI-generated outputs
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Testing the workflow with representative business cases
Practical Workshop
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Selecting a recurring business task
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Designing the workflow
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Defining process memory and state
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Executing and testing the workflow
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Comparing multiple runs
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Identifying errors and areas for improvement
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Preparing the automation for practical use
Troubleshooting
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Missing or incomplete context
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Incorrect or outdated process state
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Inconsistent results across executions
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Missing data or unavailable sources
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Duplicate processing
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Failed or partially completed workflow runs
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Limitations of tools or account features
Summary and Next Steps
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Reviewing the completed workflow prototype
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Identifying tasks suitable for automation
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Defining quality and acceptance criteria
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Planning deployment in daily operations
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Exploring opportunities for an advanced second day featuring code-supported processing and agent development
Requirements
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Pre-course Knowledge for Participants
- Familiarity with basic ChatGPT concepts
- Programming knowledge is not a prerequisite.
- Participants need a computer, internet connection, and a ChatGPT account with access to the features utilized in the workshops.
- Access to plugins, Spaces, and scheduling/automation features should be verified beforehand, as availability may depend on account or organizational settings.
Target Audience
This course is designed for:
- Specialists and managers who integrate ChatGPT into their daily operations
- Process owners and business analysts focused on enhancing team workflows
- Professionals creating reports, summaries, documents, data compilations, and regular business updates
- Team leaders adopting AI and coordinating work through shared information sources
A concise summary for the catalogue would be:
Prerequisites: Basic understanding of ChatGPT. No coding experience needed.
Audience: Managers, specialists, process owners, business analysts, reporting/documentation experts, and AI implementation leaders.
Testimonials (1)
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
