Course Outline
Block 1 — Shared Foundations (Days 1–2)
Day 1 — Morning: The Human Factor in AI Adoption for Government
• Trust and reliance calibration: determining appropriate use of AI.
• Team agreement structure: defining triggers, actions, evidence requirements, and ownership.
• Prompt Curator role: ensuring validation, decision-making, sign-off, and developing an AI incident response plan.
Day 1 — Afternoon: Constraints, Risks, and Compliance for Government
• Real LLM capabilities: identifying prompt risk vectors such as injection, data leakage, and hallucinations.
• Legal framework: understanding GDPR, EU AI Act, and sector-specific standards (e.g., DICOM, HL7, HIPAA).
• Practical exercise: translating domain standards into prompt guardrails.
Day 2 — Morning: Technical Architecture of Prompts for Government
• Agent architecture: exploring memory, context, and goals from a prompt design perspective.
• API integration and domain data sources, including multi-agent systems and prompt chaining.
Day 2 — Afternoon: Enterprise Prompt Anatomy for Government
• The six layers of prompts: Role, Context, Constraints, Domain Standards, Format, and Examples.
• Prompt hierarchy: System (organization-wide), Domain (team), and Task (individual).
• Demonstration: deconstructing a basic prompt and rebuilding it. Team briefing for Days 3–5.
Block 2 — Co-Construction Workshops (Days 3–4–5)
Day 3 — Discovery and Standards Audit for Government
- Parallel team workshops: Architects, Domain-Specific Developers, Back-End Engineers, QA Specialists.
- Mapping enterprise standards and constraints to identify cross-team conflicts.
- Day 3 Deliverable: Standards Map and impact/effort priority matrix.
Day 4 — Convention Design and Template Construction for Government
- Establishing naming conventions, versioning systems, and tagging (team, domain, target tool).
- Building the first validated templates: TypeScript DICOM, code review, QA tests, and API documentation.
- Day 4 Deliverable: Four or more operational templates and a conventions guide.
Day 5 — Library Assembly, Governance, and Official Handover for Government
- Organizing the library and integrating it with GitHub Copilot, Cursor, and internal LLM APIs.
- Defining the Prompt Curator role, establishing quality metrics, team rituals, and a 30-day deployment plan.
- Final Day 5 Deliverable: Documented Library v1.0, Governance Charter, and 30-Day Plan.
Requirements
- Completion of at least one artificial intelligence training course (either introductory or advanced).
- Technical profiles: experience in the organization’s technology stack.
- Management profiles: foundational knowledge of AI tools (such as ChatGPT, Copilot, etc.).
- Organizational commitment: active engagement of team leaders during Days 3 through 5.
- Pre-existing documentation: availability of established standards (including README files and coding guidelines).
Target Audience
- Software Architects
- Developers (domain-specific, back-end, front-end)
- Quality Assurance Engineers / Code Technicians
- Team Leaders and Middle Managers
- IT Managers, Decision-Makers, and AI Project Leads for government initiatives.
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
