Course Outline
Module 1 — Establishing Shared Foundations (Days 1–2)
Day 1 — Morning: The Human Element in AI Adoption
• Calibrating trust and reliance: determining when to leverage AI and when to cease.
• Defining team agreement structures (trigger, action, evidence, owner).
• Defining the Prompt Curator role: validation, decision-making, and sign-off processes. Developing an AI incident response plan.
Day 1 — Afternoon: Constraints, Risks, and Compliance
• Realistic LLM capabilities and associated prompt risk vectors: injection, data leakage, and hallucinations.
• Legal framework overview: GDPR, EU AI Act, and sector-specific standards (DICOM, HL7, HIPAA).
• Practical exercise: converting a domain standard into a prompt guardrail.
Day 2 — Morning: Technical Prompt Architecture
• Agent architecture perspectives: memory, context, and goals from a prompt design standpoint.
• API integration, domain data sources, multi-agent systems, and prompt chaining.
Day 2 — Afternoon: Anatomy of Enterprise Prompts
• The six core layers: Role, Context, Constraints, Domain Standards, Format, and Examples.
• Prompt hierarchy: System-level (organization-wide), Domain-level (team), and Task-level (individual).
• Demonstration: deconstructing a naive prompt and reconstructing it effectively. Briefing for Days 3–5.
Module 2 — Co-Construction Workshops (Days 3–4–5)
Day 3 — Discovery and Standards Audit
- Parallel team workshops involving Architects, Domain-Specific Developers, Back-End Teams, and QA.
- Mapping enterprise standards and constraints to identify potential cross-team conflicts.
- Day 3 Output: A Standards Map and an impact/effort priority matrix.
Day 4 — Convention Design and Template Development
- Establishing naming conventions, versioning rules, and tag systems (by team, domain, and target tool).
- Creating initial validated templates for TypeScript DICOM, code review, QA testing, and API documentation.
- Day 4 Output: Four or more operational templates accompanied by a conventions guide.
Day 5 — Library Assembly, Governance, and Official Handover
- Structuring the library and integrating with GitHub Copilot, Cursor, or internal LLM APIs.
- Finalizing the Prompt Curator role, quality metrics, team rituals, and the 30-day deployment plan.
- Day 5 Final Output: A documented Library v1.0, a Governance Charter, and a 30-Day Plan.
Requirements
- Completion of at least one prior AI training course (introductory or advanced level).
- Technical roles: hands-on experience with the company’s current technology stack.
- Management roles: basic familiarity with AI tools such as ChatGPT or Copilot.
- Organizational commitment: active involvement from team leaders during Days 3–5.
- Preparation: availability of existing standards documentation (e.g., README files, coding guidelines).
Intended audience
- Software Architects
- Developers (specialized domain, back-end, and 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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