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Course Outline
Module 1: Introduction to Artificial Intelligence Theory
- Model-Based Frameworks: Approaching AI as a disciplined engineering challenge.
- Clarifying Capabilities: Defining the operational scope and limitations of AI systems.
- Technological Progression: Tracing development from early architectures to Transformer models.
- Application Domains: Applications in analytics, creative services, research, visual media, audio, and video production.
- Data Governance Principles: Foundational requirements, audit processes, and emerging trends including multimodality, autonomous agents, Retrieval-Augmented Generation (RAG), and the comparative analysis of Large Language Models (LLMs) versus Small Language Models (SLMs).
- Ethical Considerations: Addressing intellectual property rights, algorithmic bias, model hallucinations, and vulnerabilities to social engineering.
- Risk Evaluation: Mitigating data poisoning, addressing specific threats such as Nepenthes, and managing the potential impact on workforce competency.
- Model Classification: Distinguishing between foundation models and task-specific solutions; open-weight versus proprietary architectures.
Module 2: Current Landscape and Tool Selection
- Benchmarking Leaderboards: Comparative analysis of leading language models.
- Acquisition Criteria: Evaluating cost efficiency, latency, data privacy safeguards, and vendor dependency risks for government use cases.
- Leading Commercial Models: Overview of OpenAI ChatGPT, Perplexity, Google Gemini, and xAI Grok.
- Specialized and Compact Models: Assessment of tools such as Manus and SpecKit.
- Visual Content Generation: Utilization of platforms like Perchance.
- Operational Limits: Understanding context window decay and token consumption costs.
Module 3: Interaction Design – Prompt and Context Engineering
- Validation Frameworks: Ensuring completeness, logical consistency, and verifiability of outputs.
- RAG Implementation: Determining appropriate use cases for Retrieval-Augmented Generation compared to model fine-tuning.
- Return on Investment: Balancing maintenance expenditures against productivity improvements for government agencies.
- Advanced Methodologies: Application of 20+ prompt engineering and RAG techniques with practical examples.
- Emerging Techniques: Overview of triangulation methods, Map & Terrain strategies, and model-based generation processes.
Module 4: Artificial Intelligence in Agile Project Management
- Automation Engines: Leveraging AI as a core component of operational infrastructure.
- Decision Authority: Maintaining human accountability while utilizing AI support.
- AIOps & GitOps: Integrating intelligent automation into standard operational workflows.
- Integration Pipelines: Establishing seamless, AI-enhanced development environments.
- Agile Deliverables: Managing backlogs, roadmaps, and requirements documentation.
- Estimation Accuracy: Differentiating between capacity planning precision and statistical accuracy.
- Product Ownership: Facilitating ideation, feature analysis, and identifying risks associated with low-code development methods.
- Scenario Planning: Conducting "What If" analyses and implementing automated risk management protocols.
- Requirement Refinement: Standardizing the description and validation of use cases and user stories.
Requirements
- Foundational knowledge of the Agile Manifesto and Scrum framework.
- Professional background in project management, product ownership, or team leadership roles.
- Technical expertise in programming or AI engineering is not mandatory; however, general proficiency with digital tools is advisable. This curriculum is designed specifically for government professionals seeking to enhance operational efficiency.
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 methodologies.
7 Hours
Testimonials (1)
The trainer is patient and very helpful. He knows the topic well.
