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Course Outline
Module 1: Context, Scope and Delivery Challenges
- Distinguishing between autocomplete and autonomous multi-step execution
- Common AI misconceptions in software delivery
- Why improved prompts alone are insufficient
- Identifying participant tooling, pain points, and goals
- 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 requirement extraction
- Chunking strategies: structural, semantic, and sliding-window approaches
- Preserving dependencies and cross-references
- Handling tables, diagrams, flowcharts, and mixed inputs
- Effective management of context windows
Module 3: Human Judgment Boundaries
- Identifying areas where human decision-making remains critical
- Spotting hallucinated dependencies
- Detecting fabricated constraints and inverted logic
- Preventing unsafe helpful defaults
- Validation frameworks for traceability, consistency, and completeness
Module 4: From Requirements to Code with Agentic Tools
- The architecture-first delivery model
- Component mapping and service boundaries
- API contracts as delivery anchors
- Persistent rules and constraints within AI tools
- Linking task instructions to requirements
- Comparing minimal prompting versus constrained prompting approaches
- Contract-first generation for backend and frontend components
Module 5: Agentic Iteration Loop
- The self-correction spiral
- 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: Code Quality Enforcement
- Prompt constraints for handling edge cases
- Rules documents as living governance artifacts
- Automated gates utilizing linting and static analysis
- Security scanning within AI-generated code
- Dependency and architecture conformance checks
- Human review protocols for AI outputs
Module 7: Feedback Loops and Continuous Improvement
- Feeding structured failures back into AI workflows
- 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
- Common security risks associated with generated code
- Technology-specific security rules appendices
- Pre-commit security scanning
- 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
- Domain-language test design
- Safely generating test implementations
- Concepts of mutation testing
- Validating specification coverage
- Reviewing assertion strength
- Utilizing diagnostic questioning models
Module 10: Maintaining the System
- Living artifacts: contracts, maps, rules, and test specs
- Evolving constraints over time
- AI governance for long-term maintainability
- Preventing technical debt using AI controls
- Operating models for sustainable AI engineering teams
Requirements
Participants should possess:
- Hands-on experience in 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
- Awareness of core software testing concepts
- Exposure to AI coding tools is beneficial but not mandatory
- Designed for mid-level to senior technical professionals
14 Hours
