Course Outline
Block 1 — Shared Foundations (Days 1–2)
Day 1 — Morning: The Human Factor in AI Adoption
• Trust and reliance calibration: determining when to use AI and when to stop.
• Team agreement structure (trigger / action / evidence / owner).
• Prompt Curator role: validation, decision-making, and sign-off. AI incident response plan.
Day 1 — Afternoon: Constraints, Risks and Compliance
• Real LLM capabilities — prompt risk vectors: injection, data leakage, and hallucinations.
• Legal framework: GDPR, EU AI Act, and sector standards (DICOM, HL7, HIPAA).
• Practical exercise: translating a domain standard into a prompt guardrail.
Day 2 — Morning: Technical Architecture of Prompts
• Agent architecture: memory, context, and goals from a prompt design perspective.
• API integration and domain data sources, multi-agent systems, and prompt chaining.
Day 2 — Afternoon: Enterprise Prompt Anatomy
• The six layers: Role / Context / Constraints / Domain Standards / Format / Examples.
• Prompt hierarchy: System (organization-wide) — Domain (team) — Task (individual).
• Demo: deconstructing a naive 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
- Parallel team workshops: Architects, Domain-Specific Developers, Back-End, and 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
- Establishing naming conventions, versioning, and tag systems (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.
- Defining the Prompt Curator role, quality metrics, team rituals, and the 30-day deployment plan.
- Final Day 5 Deliverable: Documented Library v1.0 + Governance Charter + 30-Day Plan.
Requirements
- Completion of at least one AI training course (introductory or advanced).
- Technical profiles: development experience within the company's tech stack.
- Management profiles: basic familiarity with AI tools (e.g., ChatGPT, Copilot).
- Company commitment: active participation of team leaders on Days 3–5.
- Prior provision: existing standards documentation (README files, 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
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
