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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).
  • 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.

Požadavky

  • 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
 35 Hodiny

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