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Course Outline
Module 1: Introduction & AI Theory
- The Model-Based Approach: Treating AI as an engineering problem.
- Demystifying the "Ghost in the Machine": Distinguishing what AI is versus what it is not.
- The Evolution of Technology: From BERT to Transformers.
- Generative Domains: Analysis, Creative, Research, Image, Music, and Video applications.
- Data Governance: Pillars, audits, and emerging research trends (Multimodality, Agents, RAG, LLM vs. SLM).
- The Dark Side: Ethics, Intellectual Property (IP), bias, hallucinations, and social engineering risks.
- Risk Assessment: Data poisoning, Nepenthes, and the risk of "dumbing down" human talent.
- Model Taxonomy: Foundation vs. Task-specific models; Closed vs. Open-weight models.
Module 2: Current Landscape & Toolset
- The Language Models Arena: Comparing performance metrics and benchmarks.
- Professional Purchase Criteria: Evaluating cost, latency, privacy, and vendor lock-in.
- Overview of Major Models: OpenAI ChatGPT, Perplexity, Gemini, and Grok.
- Niche & Small Models: Manus, SpecKit.
- Graphical Generation Tools: Perchance.
- Technical Constraints: Context rot versus Token cost.
Module 3: Interaction - Prompt & Context Engineering
- The Verification Framework: Ensuring completeness, consistency, and verifiability.
- The RAG Strategy: Determining when to use Retrieval-Augmented Generation versus fine-tuning.
- ROI of AI: Balancing maintenance costs against productivity gains.
- Advanced Techniques: Exploring 20+ Prompt & RAG methods with real-world examples.
- Experimental Frontiers: Triangulation, Map & Terrain overviews, and Model-based generation.
Module 4: AI in Agile Project Management
- The Supercomputer Pilot: Positioning AI as an automation engine.
- Decision Making: Balancing human responsibility with AI assistance.
- AIOps & GitOps: Integrating AI into operational workflows.
- Toolchains & Pipelines: Building a seamless AI-driven environment.
- Agile Artifacts: Managing backlog, roadmap, and requirements engineering.
- Precision Management: Capacity planning and estimation (distinguishing Accuracy vs. Precision).
- Product Ownership: Ideation, feature analysis, and assessing Vibe-coding risks.
- Risk & Scenarios: Planning for contingencies and automating risk management.
- Refinement: Describing and refining Use Cases and User Stories.
Requirements
- A foundational understanding of the Agile Manifesto and Scrum framework.
- Experience in project management, product ownership, or team leadership roles.
- No prior programming or AI engineering experience is necessary, although 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 operating within Agile environments.
- Operations Managers with an interest in AIOps.
7 Hours
Testimonials (3)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
Although the time allocated was very short, only 4 hours, it still managed to generate value.
Eugen Floarea - Mateco
Course - Introduction to Artificial Intelligence for Non-technical users
Machine Translated
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
