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Course Outline
Module 1: Context, Scope and Delivery Challenges
- Distinguishing between autocomplete and autonomous multi-step execution
- Common misconceptions about AI in software delivery
- Why improved prompting alone is 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 requirement extraction
- Chunking strategies: structural, semantic, and sliding-window approaches
- Preserving dependencies and cross-references
- Working with tables, diagrams, flowcharts, and mixed inputs
- Effective management of context windows
Module 3: Human Judgment Boundaries
- Areas where human decision-making remains critical
- Identifying 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
- 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 vs. constrained prompting approaches
- Contract-first backend and frontend generation
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 edge cases
- Rules documents as living governance artifacts
- Automated gates with linting and static analysis
- Security scanning in AI-generated code
- Dependency and architecture conformance checks
- Human review protocol for AI outputs
Module 7: Feedback Loops and Continuous Improvement
- Incorporating 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 in generated code
- Technology-specific security rules appendices
- Pre-commit security scanning
- Secure SDLC controls for AI-assisted development
- Human accountability in secure delivery
Module 9: Testing Anchored to Specifications
- Generating test specifications from requirements
- Domain-language test design
- Safely generating test implementations
- Mutation testing concepts
- Specification coverage validation
- Assertion-strength review
- Diagnostic questioning models
Module 10: Maintaining the System
- Living artifacts: contracts, maps, rules, test specs
- Evolving constraints over time
- AI governance for long-term maintainability
- Preventing technical debt using AI controls
- Operating model for sustainable AI engineering teams
Requirements
Participants should have:
- Experience in software development projects
- Understanding of application architecture fundamentals
- Familiarity with APIs, backend/frontend systems, or full-stack delivery
- Basic knowledge of Agile or iterative software delivery
- Awareness of software testing concepts
- Exposure to AI coding tools is helpful but not mandatory
- Suitable for mid-level to senior technical professionals
14 Hours
