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Course Outline

Day 1: Foundational Concepts and Trusted Application of Generative AI

Core principles of AI and GenAI: definitions, operational mechanics, value propositions, and limitations

Effective prompting strategies: leveraging standardized structures, precise inputs, defined constraints, and formatted outputs

Iterative refinement methodologies: enhancing results through feedback mechanisms and structured directives

Quality assurance and validation: utilizing checklists, cross-verification, assumption testing, traceability, and acceptance standards

Standardization of deliverables: employing templates for technical memoranda, executive summaries, reports, and action items

Documentation and requirements engineering: drafting, editing, structuring, summarizing, and formulating change orders and requirements

Ethical use and data security: adhering to confidentiality protocols, intellectual property safeguards, governance frameworks, and safe-use mandates

Practical application using realistic, anonymized government scenarios for for government operations


Day 2: Application Scenarios, Efficiency Gains, and Workflow Integration

Analysis and reporting: transforming raw data into structured insights and executive-ready briefings

Problem resolution and troubleshooting: utilizing AI-assisted root cause analysis and strategic action planning

Cross-functional coordination: ensuring decision clarity, effective handovers, comprehensive meeting records, and stakeholder alignment

AI-enabled code and automation support: securely generating and reviewing code snippets, pseudocode, and test logic

Accelerating knowledge work: developing reusable procedures, internal standards, and institutional knowledge repositories

Workflow integration: establishing repeatable end-to-end processes from request initiation to deliverable completion, including validation protocols

Prompt libraries and checklists: implementing role-specific collections to enhance consistency and adoption rates

Capstone exercise and 30-day implementation strategy: converting one practical case per participant into a sustainable workflow, identifying quick wins and establishing simple measurement metrics

Requirements

This instructional program targets engineering personnel, technical practitioners, and operational staff responsible for documentation, process adherence, data-centric decision-making, and interdepartmental collaboration. It is appropriate for specialists and team supervisors seeking to enhance productivity and output quality by leveraging Generative AI in routine assignments, without the need for advanced programming or data science expertise. The curriculum also addresses operational and business support roles that regularly engage with technical data and require the production of clearer, more rapid, and consistent deliverables, providing essential capabilities for government agencies.

 14 Hours

Number of participants


Price per participant

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