Εξέλιξη Κομματιού
Introduction
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What is business automation with ChatGPT?
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Models, agents, tools, and workflows
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Fixed workflows vs agentic processes
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Choosing the right level of autonomy
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The role of human review and decision-making
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Beyond a single ChatGPT conversation: reusable workflows and recurring execution
Understanding Context and Memory
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Chat context vs persistent memory
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Explicitly stored process state
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Preserving decisions and important information between runs
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Context limitations and loss of earlier information
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Continuing work across multiple conversations
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Distinguishing permanent rules from current process status
Organizing Work in ChatGPT
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The risks of combining too many tasks and changing requirements in one thread
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Recognizing missing decisions and inconsistent outputs
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Splitting work into smaller, clearly defined tasks
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Passing goals, sources, decisions, and results between tasks
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When to use separate conversations or workflows
Working with Plugins, Projects, and Spaces
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What plugins, projects, and Spaces are used for
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Organizing reusable instructions and tools
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Connecting workflows to business information sources
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Organizing related conversations and shared materials
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Sharing documentation and results
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Preparing the working environment for a business process
Building a Workflow with Memory
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Defining the workflow objective
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Identifying inputs, steps, outputs, and acceptance criteria
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Recording process state between executions
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Using previous decisions in subsequent runs
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Handling errors and incomplete execution
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Resuming a workflow after interruption
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Building a prototype workflow for a selected business task
Scheduling and Repeatable Execution
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Manual vs recurring workflow execution
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Defining execution frequency and time zone
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Notification and stopping rules
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Handling cases where no new data is available
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Re-running a workflow with saved state
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Comparing repeated executions
Evaluating Workflow Quality and Repeatability
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Defining quality criteria
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Checking required fields, numbers, sources, and output format
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Identifying duplicate or inconsistent results
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Distinguishing acceptable wording differences from process errors
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Understanding sources of variability in AI-generated results
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Testing the workflow on 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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Running and testing the workflow
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Comparing multiple executions
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Identifying errors and improvement opportunities
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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 between 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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Tool or account feature limitations
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 work
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Identifying opportunities for a more advanced second-day version with code-supported processing and agent development
Απαιτήσεις
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What delegates should know prior to the course
- Basic familiarity with ChatGPT
- No programming knowledge is specified as a prerequisite.
- Delegates should have a computer, internet connection, and a ChatGPT account with access to the features used during the workshops.
- Availability of plugins, Spaces, and scheduling/automation features should be checked before the course, as access can depend on account and organizational settings.
Audience
The course is addressed to:
- Specialists and managers using ChatGPT in their daily work
- Process owners and business analysts responsible for improving team workflows
- People preparing reports, summaries, documents, data compilations, and recurring business updates
- Team leaders implementing AI and organizing work with shared information sources
A short catalogue-style version could be:
Prerequisites: Basic knowledge of ChatGPT. No programming experience required.
Audience: Managers, specialists, process owners, business analysts, reporting/documentation professionals, and AI implementation leaders.
Σχόλια (1)
Προσαρμόζεται στις προτάσεις του κοινού, όπως η δημιουργία ενός πραγματικού σεναρίου με τεχνητή νοημοσύνη στην ώρα του.
Brett McLaren - Zoll Itamar
Κομμάτι - ChatGPT for Productivity: A Beginner’s Guide
Μηχανική Μετάφραση
