Course Outline
Block 1 — Shared Foundations (Days 1–2)
Day 1 — Morning: The Human Element in AI Adoption
• Calibrating trust and reliance: Determining when to use AI and when to refrain.
• Structuring team agreements: Defining triggers, actions, evidence, and ownership.
• The Prompt Curator role: Focusing on validation, decision-making, and sign-off. Reviewing the AI incident response plan.
Day 1 — Afternoon: Constraints, Risks, and Compliance
• Real-world LLM capabilities: Analyzing prompt risk vectors such as injection, data leakage, and hallucinations.
• Legal framework overview: GDPR, the EU AI Act, and sector standards (DICOM, HL7, HIPAA).
• Practical exercise: Translating domain standards into effective prompt guardrails.
Day 2 — Morning: Technical Architecture of Prompts
• Agent architecture: Exploring memory, context, and goals from a prompt design perspective.
• API integration, domain data sources, multi-agent systems, and prompt chaining.
Day 2 — Afternoon: Enterprise Prompt Anatomy
• The six layers: Role, Context, Constraints, Domain Standards, Format, and Examples.
• Prompt hierarchy: System (organization-wide), Domain (team-level), and Task (individual).
• Demonstration: Deconstructing a basic prompt and rebuilding it. Introduction of the team brief for Days 3–5.
Block 2 — Co-Construction Workshops (Days 3–4–5)
Day 3 — Discovery and Standards Audit
- Parallel team workshops involving Architects, Domain-Specific Developers, Back-End Engineers, and QA.
- Mapping enterprise standards and constraints while identifying conflicts across teams.
- Day 3 Deliverable: A Standards Map accompanied by an impact/effort priority matrix.
Day 4 — Convention Design and Template Construction
- Establishing naming conventions, versioning strategies, and tag systems (team, domain, target tool).
- Creating initial validated templates for TypeScript DICOM, code reviews, QA tests, and API documentation.
- Day 4 Deliverable: Four or more operational templates and a conventions guide.
Day 5 — Library Assembly, Governance, and Official Handover
- Organizing the library and integrating with GitHub Copilot, Cursor, or internal LLM APIs.
- Defining the Prompt Curator role, quality metrics, team rituals, and the 30-day deployment plan.
- Final Day 5 Deliverable: A documented Library v1.0, a Governance Charter, and the 30-Day Plan.
Requirements
- Completion of at least one previous AI training (introductory or advanced level).
- For technical roles: development experience within the company's technology stack.
- For management roles: basic familiarity with AI tools such as ChatGPT or Copilot.
- Organizational commitment: Active participation from team leaders during Days 3–5.
- Pre-requisite documentation: Existing standards documents such as READMEs and coding guides must be provided.
Target audience
- Software architects
- Developers (domain-specific, back-end, and front-end)
- QA engineers and code technicians
- Team leaders and middle managers
- IT managers, decision-makers, and AI project leads
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 7250 € + VAT*
Contact us for an exact quote and to hear our latest promotions
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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
