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Course Outline
Module 1: Introduction & AI Theory
- The Model-Based Approach: AI as an engineering challenge.
- Demystifying the "Ghost in the Machine": What AI is versus what it is not.
- The Evolution of Technology: From BERT to Transformers.
- Generative Domains: Analysis, Creativity, Research, Image, Music, and Video.
- Data Governance: Pillars, audits, and emerging research trends (Multimodality, Agents, RAG, LLM versus SLM).
- The Dark Side: Ethics, intellectual property, bias, hallucinations, and social engineering.
- Risk Assessment: Data poisoning, Nepenthes, and the risk of diminishing human talent.
- Model Taxonomy: Foundation versus task-specific; Closed versus 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.
- Overview of Large Models: OpenAI ChatGPT, Perplexity, Gemini, and Grok.
- Niche & Small Models: Manus, SpecKit.
- Graphical Generation: Perchance.
- Technical Constraints: Context rot versus token cost.
Module 3: Interaction - Prompt & Context Engineering
- The Verification Framework: Completeness, consistency, and verifiability.
- The RAG Strategy: When to use Retrieval-Augmented Generation versus fine-tuning.
- ROI of AI: Maintenance costs versus 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 versus 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 versus 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
- A basic understanding of the Agile Manifesto and the Scrum framework.
- Experience in project management, product ownership, or team leadership.
- No prior programming or AI engineering experience is required, though 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)
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
Able to pivot upon audience suggestions - ie able to create a real AI agent scenario on the spot.
