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Course Outline
Module 1: Introduction & AI Theory
- The Model-Based Approach: AI as an engineering problem.
- Demystifying the "Ghost in the Machine": What AI is vs. what it is not.
- The Evolution of Tech: From BERT to Transformers.
- Generative Domains: Analysis, Creative, Research, Image, Music, and Video.
- Data Governance: Pillars, audits, and the research trends (Multimodality, Agents, RAG, LLM vs. SLM).
- The Dark Side: Ethics, IP, bias, hallucinations, and social engineering.
- Risk Assessment: Data poisoning, Nepenthes, and the risk of "dumbing down" human talent.
- Model Taxonomy: Foundation vs. Task-specific; Closed vs. Open-weight models.
Module 2: Current Landscape & Toolset
- The Language Models Arena: Comparing performance and benchmarks.
- Professional Purchase Criteria: Cost, latency, privacy, and vendor lock-in.
- Big Models Overview: OpenAI ChatGPT, Perplexity, Gemini, and Grok.
- Niche & Small Models: Manus, SpecKit.
- Graphical Generation: Perchance
- Technical Constraints: Context rot vs. Token cost.
Module 3: Interaction - Prompt & Context Engineering
- The Verification Framework: Completeness, consistency, and verifiability.
- The RAG Strategy: When to use Retrieval-Augmented Generation vs. fine-tuning.
- ROI of AI: Maintenance costs vs. productivity gains.
- Advanced Techniques: 20+ Prompt & RAG methods with real-world examples.
- Experimental Frontiers: Triangulation, Map & Terrain overview, and Model-based generation.
Module 4: AI in Agile Project Management
- The Supercomputer Pilot: AI as an automation engine.
- Decision Making: Human responsibility vs. AI assistance.
- AIOps & GitOps: Integrating AI into the operational workflow.
- Toolchains & Pipelines: Creating a seamless AI-driven environment.
- Agile Artifacts: Backlog, roadmap, and requirements engineering.
- Precision Management: Capacity planning and estimation (Accuracy vs. Precision).
- Product Ownership: Ideation, feature analysis, and Vibe-coding risks.
- Risk & Scenarios: Planning for "What Ifs" and automated risk management.
- Refinement: Use Case and User Story description & refinement.
Requirements
- Basic understanding of the Agile Manifesto and Scrum framework.
- Experience in project management, product ownership, or team leadership.
- No prior programming or AI engineering experience is required, though a general familiarity with digital tools is recommended.
Audience
- Agile Project Managers and Scrum Masters.
- Product Owners and Product Managers.
- IT Team Leaders and Delivery Managers.
- Business Analysts working in Agile environments.
- Operations Managers interested in AIOps.
7 Hours
Testimonials (2)
Hands on examples
Ryan Brookman - The Shaw Group Limited
Course - Introduction to Artificial Intelligence for Non-technical users
We got to use the tools.
