Course Outline
Module 1 — Foundational Principles (Days 1–2)
Day 1 — Morning: Human-Centric AI Integration
• Trust and reliance calibration: Establishing criteria for AI utilization and intervention.
• Team alignment structure: Defining triggers, actions, evidence, and accountability.
• Prompt Curator responsibilities: Validation, decision-making, and approval processes; implementation of an AI incident response protocol.
Day 1 — Afternoon: Regulatory Constraints, Risk Management, and Compliance
• Large Language Model (LLM) capabilities and associated risk vectors: Prompt injection, data leakage, and hallucination mitigation.
• Legal and regulatory framework: GDPR, EU AI Act, and sector-specific standards (e.g., DICOM, HL7, HIPAA).
• Applied exercise: Translating domain-specific standards into robust prompt guardrails.
Day 2 — Morning: Prompt Engineering Technical Architecture
• Agent architecture components: Memory, context, and objectives viewed through a prompt design lens.
• API integration strategies: Utilizing domain data sources, multi-agent systems, and prompt chaining techniques.
Day 2 — Afternoon: Enterprise-Grade Prompt Structure
• The six-layer framework: Role, Context, Constraints, Domain Standards, Format, and Examples.
• Prompt hierarchy management: System-level (organization-wide), Domain-level (team-specific), and Task-level (individual) directives.
• Demonstration: Deconstructing and reconstructing basic prompts; issuing operational briefings for Days 3–5.
Module 2 — Collaborative Development Workshops (Days 3–5)
Day 3 — Discovery and Standards Assessment
- Concurrent team workshops: Architects, Domain-Specific Developers, Back-End Engineers, and QA Specialists.
- Mapping enterprise standards and constraints; identifying and resolving cross-team conflicts.
- Day 3 Outcome: Comprehensive Standards Map and an impact/effort priority matrix.
Day 4 — Protocol Design and Template Development
- Establishing naming conventions, version control protocols, and tagging systems (by team, domain, and tool).
- Development of initial validated templates: TypeScript DICOM handling, code review protocols, QA test cases, and API documentation.
- Day 4 Outcome: Four or more operational templates and a standardized conventions guide.
Day 5 — Library Integration, Governance Framework, and Formal Transition
- Library organization and integration with GitHub Copilot, Cursor, or internal LLM APIs.
- Establishment of the Prompt Curator role, quality metrics, team operational rituals, and a 30-day deployment roadmap.
- Day 5 Final Outcome: Documented Library v1.0, Governance Charter, and 30-Day Implementation Plan.
Requirements
- Completion of at least one prior AI training module (introductory or advanced level).
- Technical roles: Demonstrated development experience within the organization’s technology stack.
- Management roles: Basic proficiency with AI tools (e.g., ChatGPT, Copilot).
- Organizational commitment: Active participation of team leaders during Days 3–5.
- Prior preparation: Availability of existing standards documentation (e.g., README files, coding guides).
Target Audience
- Software Architects
- Developers (Domain-specific, Back-end, Front-end)
- QA Engineers and Code Technicians
- Team Leaders and Middle Managers
- IT Managers, Decision-Makers, and AI Project Leads
Testimonials (1)
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