ChatGPT Automation and Workflow Optimisation Training Course

Participants can design prompt specifications that produce consistent, reviewable and structured outputs.

• Prompt architectures using role, task, context, constraints and output format
• Few-shot examples and delimiters for reliable structured responses
• System instructions distinguished from user inputs and reference material
• JSON schemas, tables and markdown templates for machine-readable outputs
• Test prompt variants against representative cases and acceptance criteria
• Version prompts with change logs, owners and approval status

Participants can configure ChatGPT features for recurring analysis, drafting and knowledge tasks.

• Custom GPT configuration: instructions, knowledge files, actions and guardrails
• Data analysis workspace for spreadsheets, anomalies and report drafts
• File-based synthesis with citation checks and source traceability
• Projects and Canvas for iterative document production
• Task automation boundaries between ChatGPT interface and external workflows
• Output review patterns for factual, numerical and policy-sensitive content

Participants can specify the technical components required for dependable AI workflow automation.

• OpenAI API request structure: messages, parameters, tools and responses
• Temperature, token limits and model selection against task requirements
• Function calling to connect approved systems and structured actions
• Webhooks, authentication and error handling in automated workflow chains
• Idempotency, retries and rate-limit handling for dependable automations
• Cost estimation from token usage, call volumes and model pricing

Participants can design no-code flows that route ChatGPT outputs through business processes.

• Zapier and Make scenarios triggered by forms, email and records
• Map fields, filters, routers and formatters between connected applications
• Human approval steps before consequential messages or record updates
• Build exception paths for missing inputs, failures and escalations
• Schedule recurring summaries, reminders and operational status reports
• Log workflow runs for diagnosis, audit and continuous improvement

Participants can apply practical controls for data handling, human oversight and AI risk management.

• Data classification separates public, internal, confidential and restricted information
• Minimise prompts to necessary fields, redacting personal and sensitive data
• Retention, access control and data residency requirements mapped before deployment
• OWASP prompt injection risks addressed through input and tool controls
• Human-in-the-loop thresholds for financial, legal and personnel decisions
• Audit trails record source data, prompts, outputs, approvals and changes

Participants can design grounded knowledge assistants that answer from approved source material.

• Retrieval-augmented generation architecture: chunking, embeddings, retrieval and grounded responses
• Knowledge-source selection based on authority, freshness, ownership and permissions
• Chunk size and metadata design for precise retrieval results
• Citation requirements and abstention rules reduce unsupported answers
• Evaluate retrieval quality with relevance, coverage and faithfulness measures
• Maintain knowledge bases through refresh schedules and content ownership

Participants can test AI workflows against quality, safety and operational acceptance criteria.

• Test cases cover normal, ambiguous, adversarial and edge-condition inputs
• Acceptance criteria define required fields, tone, citations and escalation behaviour
• Measure precision, completeness, hallucination rate and reviewer correction effort
• Compare baseline and automated workflows using time-and-quality metrics
• Failure mode and effects analysis identifies control priorities
• Release checklists govern pilot approval, rollback and change communication

Participants can select appropriate ChatGPT automation patterns for common business functions.

• Accounts-payable triage extracts invoice fields and routes exceptions
• Service-desk summarisation drafts responses while preserving escalation ownership
• Procurement analysis compares supplier submissions against stated requirements
• Policy and procedure assistants answer only from approved knowledge sources
• HR communications automation applies approved language and human sign-off
• Choose patterns appropriate to banking, public-sector, NGO and corporate controls

Participants can prepare an accountable plan for piloting and operating an AI automation service.

• Automation operating model assigns process owner, prompt owner and technical owner
• RACI matrices clarify approvals, monitoring, incident response and change authority
• Service-level measures cover latency, availability, accuracy and exception resolution
• Monitor drift, feedback trends, costs and control breaches after release
• Create a 90-day implementation plan with milestones and dependencies
• Present a governed automation proposal using benefits, risks and decision gates