Schulungsübersicht
Block 1 — Shared Foundations (Days 1–2)
Day 1 — Morning: The Human Factor in AI Adoption
• Trust / reliance calibration: when to use AI, when to stop.
• Team agreement structure (trigger / action / evidence / owner).
• Prompt Curator role: validation, decision, sign-off. AI incident response plan.
Day 1 — Afternoon: Constraints, Risks and Compliance
• Real LLM capabilities — prompt risk vectors: injection, data leakage, hallucinations.
• Legal framework: GDPR, EU AI Act — sector standards (DICOM, HL7, HIPAA).
• Practical exercise: translate a domain standard into a prompt guardrail.
Day 2 — Morning: Technical Architecture of Prompts
• Agent architecture: memory, context, goals — from a prompt design perspective.
• API integration and domain data sources, multi-agent and prompt chaining.
Day 2 — Afternoon: Enterprise Prompt Anatomy
• The 6 layers: Role / Context / Constraints / Domain Standards / Format / Examples.
• Prompt hierarchy: System (org-wide) — Domain (team) — Task (individual).
• Demo: deconstruct a naive prompt, rebuild it. Team brief for Days 3–5.
Block 2 — Co-Construction Workshops (Days 3–4–5)
Day 3 — Discovery and Standards Audit
- Parallel team workshops: Architects, Domain-Specific Devs, Back-End, QA.
- Mapping enterprise standards and constraints — identifying cross-team conflicts.
- Day 3 Deliverable: Standards Map + impact/effort priority matrix.
Day 4 — Convention Design and Template Construction
- Naming conventions, versioning, tag system (team, domain, target tool).
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Building first validated templates: TypeScript DICOM, code review, QA tests, API
documentation. - Day 4 Deliverable: 4+ operational templates + conventions guide.
Day 5 — Library Assembly, Governance and Official Handover
- Library organization, GitHub Copilot / Cursor / internal LLM API integration.
- Prompt Curator role, quality metrics, team rituals, 30-day deployment plan.
- Final Day 5 Deliverable: Documented Library v1.0 + Governance Charter + 30-Day Plan.
Voraussetzungen
- Having completed at least one AI training (introductory or advanced).
- Technical profiles: development experience in the company's stack.
- Management profiles: basic familiarity with AI tools (ChatGPT, Copilot, etc.).
- Company commitment: active participation of team leaders in Days 3–5.
- Prior provision: existing standards documentation (README, coding guides).
Target audience
- Software architects
- Developers (domain-specific, back-end, front-end)
- QA engineers / Code technicians
- Team leaders and middle managers
- IT managers, decision-makers and AI project leads
Erfahrungsberichte (2)
Ich habe Wissen über die Streamlit-Bibliothek von Python erworben und werde sie sicherlich verwenden, um Anwendungen in meinem Team zu verbessern, die mit R Shiny erstellt wurden.
Michal Maj - XL Catlin Services SE (AXA XL)
Kurs - GitHub Copilot for Developers
Maschinelle Übersetzung
Der Trainer kann während des Trainings den Kursniveau anpassen, um unserem Verständnis der Thematik gerecht zu werden. Dadurch können wir nützlichere Kenntnisse erwerben, die uns helfen, die Tools in unserer täglichen Arbeit besser einzusetzen.
Tatt Juen - ViTrox Technologies Sdn Bhd
Kurs - Intermediate GitHub Copilot
Maschinelle Übersetzung
