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Duration 7 hours
Course Outline
Module 1: Introductory Concepts & AI Theory
- The Model-Based Perspective: Treating AI as an engineering challenge.
- Clarifying the "Ghost in the Machine": Distinguishing what AI is from what it is not.
- Technological Evolution: The progression from BERT to Transformers.
- Generative Sectors: Analysis, Creative, Research, Image, Music, and Video.
- Data Governance: Core pillars, audits, and emerging trends (Multimodality, Agents, RAG, LLM vs. SLM).
- Critical Challenges: Ethics, Intellectual Property, bias, hallucinations, and social engineering.
- Risk Evaluation: Data poisoning, Nepenthes, and the potential for diminishing human talent.
- Model Classification: Foundation vs. Task-specific; Closed vs. Open-weight models.
Module 2: Current Landscape & Toolset
- The Language Model Arena: Comparative analysis of performance and benchmarks.
- Professional Procurement Criteria: Cost, latency, privacy, and vendor lock-in.
- Major Models Overview: OpenAI ChatGPT, Perplexity, Gemini, and Grok.
- Niche & Smaller Models: Manus, SpecKit.
- Visual Generation: Perchance
- Technical Limitations: Context degradation vs. Token expenditure.
Module 3: Interaction - Prompt & Context Engineering
- Verification Framework: Ensuring completeness, consistency, and verifiability.
- RAG Strategy: Determining when to use Retrieval-Augmented Generation versus fine-tuning.
- ROI of AI: Balancing maintenance costs against productivity improvements.
- Sophisticated Methods: Over 20 Prompt & RAG techniques with practical 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: Balancing human responsibility with AI assistance.
- AIOps & GitOps: Embedding AI into operational workflows.
- Toolchains & Pipelines: Establishing a seamless AI-driven environment.
- Agile Artifacts: Managing the backlog, roadmap, and requirements engineering.
- Precision Management: Capacity planning and estimation (distinguishing Accuracy from Precision).
- Product Ownership: Ideation, feature analysis, and assessing Vibe-coding risks.
- Risk & Scenarios: Planning for contingencies and automating risk management.
- Refinement: Description and refinement of Use Cases and User Stories.
Requirements
- Fundamental grasp of the Agile Manifesto and the Scrum framework.
- Background in project administration, product ownership, or team leadership.
- Previous programming or AI engineering experience is not a prerequisite, although general proficiency with digital tools is advised.
Target Audience
- Agile Project Managers and Scrum Masters.
- Product Owners and Product Managers.
- IT Team Leaders and Delivery Managers.
- Business Analysts operating within Agile contexts.
- Operations Managers with an interest in AIOps.
Testimonials (1)
Understand AI function n tools to make our job easier. Need to improved AI Chubb such as make analysis n creating presentation
