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Course Outline
Module 1: Context, Scope, and Delivery Challenges
- Distinguishing between autocomplete and autonomous multi-step execution
- Addressing common misconceptions about AI in software delivery
- Understanding why improved prompts alone are insufficient
- Identifying participant tooling, pain points, and objectives
- Selecting the appropriate AI operating model for engineering teams
Module 2: Specification Ingestion and Structured Decomposition
- Creating a structural inventory of stakeholder documents
- Techniques for extracting requirements
- Chunking strategies: structural, semantic, and sliding-window approaches
- Maintaining dependencies and cross-references
- Handling mixed inputs such as tables, diagrams, and flowcharts
- Effectively managing context windows
Module 3: Boundaries of Human Judgment
- Identifying areas where human decision-making remains critical
- Recognizing hallucinated dependencies
- Detecting fabricated constraints and inverted logic
- Mitigating unsafe defaults for helpfulness
- Implementing validation frameworks for traceability, consistency, and completeness
Module 4: Transforming Requirements into Code Using Agentic Tools
- Adopting an architecture-first delivery model
- Mapping components and defining service boundaries
- Leveraging API contracts as delivery anchors
- Establishing persistent rules and constraints within AI tools
- Linking task instructions to specific requirements
- Comparing minimal prompting with constrained prompting approaches
- Generating backend and frontend code based on contracts first
Module 5: The Agentic Iteration Loop
- Navigating the self-correction spiral
- Implementing controlled iterative delivery cycles
- Reviewing diffs and code changes
- Detecting scope creep and unauthorized modifications
- Managing limited context memory
- Leveraging iteration history for continuous improvement
Module 6: Enforcing Code Quality
- Applying prompt constraints to edge cases
- Treating rules documents as living governance artifacts
- Utilizing automated gates with linting and static analysis
- Conducting security scans on AI-generated code
- Verifying conformance of dependencies and architecture
- Establishing human review protocols for AI outputs
Module 7: Feedback Loops and Continuous Improvement
- Incorporating structured failures back into AI workflows
- Defining bounded iterations and stop criteria
- Logging cycles and outcomes
- Refining rules documents over time
- Building reusable engineering intelligence
Module 8: Security Anti-Patterns in AI Delivery
- Identifying common security risks in generated code
- Utilizing technology-specific security rules appendices
- Implementing pre-commit security scanning
- Applying secure SDLC controls for AI-assisted development
- Ensuring human accountability in secure delivery
Module 9: Testing Anchored to Specifications
- Generating test specifications directly from requirements
- Designing tests using domain-specific languages
- Safely generating test implementations
- Understanding mutation testing concepts
- Validating specification coverage
- Reviewing assertion strength
- Evaluating diagnostic questioning models
Module 10: System Maintenance
- Maintaining living artifacts: contracts, maps, rules, and test specifications
- Adapting constraints over time
- Implementing AI governance for long-term maintainability
- Preventing technical debt using AI controls
- Defining an operating model for sustainable AI engineering teams
Requirements
Participants are expected to have:
- Experience with software development projects
- A solid understanding of application architecture fundamentals
- Familiarity with APIs, backend/frontend systems, or full-stack delivery processes
- Basic knowledge of Agile or iterative software delivery methodologies
- An awareness of core software testing concepts
- Exposure to AI coding tools is beneficial but not required
- The course is suitable for mid-level to senior technical professionals
14 Hours
