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Course Outline
Module 1: Introduction & AI Theory
- The Model-Based Approach: Addressing AI as an engineering challenge for government.
- Clarifying the "Ghost in the Machine": Understanding what AI is and what it is not for government operations.
- Technological Evolution: From BERT to Transformers, enhancing capabilities for government applications.
- Generative Domains: Exploring analysis, creative, research, image, music, and video generation for government use.
- Data Governance: Key pillars, audits, and emerging trends (Multimodality, Agents, RAG, LLM vs. SLM) in data management for government.
- Ethical Considerations: Addressing ethics, intellectual property, bias, hallucinations, and social engineering for government applications.
- Risk Assessment: Evaluating risks such as data poisoning, Nepenthes, and the potential impact on human talent for government operations.
- Model Taxonomy: Distinguishing between foundation models and task-specific models; closed vs. open-weight models for government use.
Module 2: Current Landscape & Toolset
- Language Models Overview: Comparing performance and benchmarks of leading models for government applications.
- Professional Purchase Criteria: Assessing cost, latency, privacy, and vendor lock-in for government procurement.
- Big Models Overview: Evaluating OpenAI ChatGPT, Perplexity, Gemini, and Grok for government use.
- Niche & Small Models: Reviewing Manus and SpecKit for specialized government needs.
- Graphical Generation: In-depth analysis of Stable Diffusion for government applications.
- Technical Constraints: Addressing context rot and token cost in government AI implementations.
Module 3: Interaction - Prompt & Context Engineering
- Verification Framework: Ensuring completeness, consistency, and verifiability in AI outputs for government.
- RAG Strategy: Determining when to use Retrieval-Augmented Generation versus fine-tuning for government projects.
- ROI of AI: Balancing maintenance costs with productivity gains for government operations.
- Advanced Techniques: Over 20 prompt and RAG methods with real-world examples for government use.
- Experimental Frontiers: Exploring triangulation, map and terrain overviews, and model-based generation for government applications.
Module 4: AI in Agile Project Management
- Supercomputer Pilot: Utilizing AI as an automation engine in government projects.
- Decision Making: Balancing human responsibility with AI assistance for government operations.
- AIOps & GitOps: Integrating AI into operational workflows for government efficiency.
- Toolchains & Pipelines: Creating a seamless AI-driven environment for government processes.
- Agile Artifacts: Managing backlogs, roadmaps, and requirements engineering in government projects.
- Precision Management: Enhancing capacity planning and estimation (Accuracy vs. Precision) for government initiatives.
- Product Ownership: Facilitating ideation, feature analysis, and mitigating Vibe-coding risks in government product development.
- Risk & Scenarios: Planning for "What Ifs" and implementing automated risk management in government projects.
- Refinement: Describing and refining use cases and user stories for government applications.
Requirements
- A foundational understanding of the Agile Manifesto and the Scrum framework is necessary.
- Practical experience in project management, product ownership, or team leadership is required.
- While prior programming or AI engineering experience is not mandatory, a basic familiarity with digital tools is advisable.
Audience
- Agile Project Managers and Scrum Masters for government projects.
- Product Owners and Product Managers.
- IT Team Leaders and Delivery Managers.
- Business Analysts operating in Agile environments.
- Operations Managers with an interest 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.
