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Course Outline
Module 1: Context, Scope, and Delivery Challenges
- Autocomplete vs. autonomous multi-step execution
- Common AI misconceptions 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
- Maintaining dependencies and cross-references
- Handling tables, diagrams, flowcharts, and mixed inputs
- Effective management of context windows
Module 3: Boundaries of Human Judgment
- Identifying areas where human decision-making remains critical
- Detecting hallucinated dependencies
- Recognizing fabricated constraints and inverted logic
- Avoiding unsafe, overly 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 defining service boundaries
- Using API contracts as delivery anchors
- Implementing persistent rules and constraints in AI tools
- Linking task instructions directly to requirements
- Minimal prompting vs. constrained prompting approaches
- Contract-first backend and frontend generation
Module 5: The Agentic Iteration Loop
- The self-correction spiral
- Controlled iterative delivery cycles
- Reviewing diffs and code changes
- Identifying scope creep and unauthorized modifications
- Managing limited context memory
- Leveraging iteration history for continuous improvement
Module 6: Enforcing Code Quality
- Applying prompt constraints for edge cases
- Treating rules documents as living governance artefacts
- Automated gates using linting and static analysis
- Security scanning of AI-generated code
- Checks for dependency and architecture conformance
- Human review protocols for AI outputs
Module 7: Feedback Loops and Continuous Improvement
- Feeding 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
- Common security risks in generated code
- Technology-specific security rule appendices
- Pre-commit security scanning
- Secure SDLC controls for AI-assisted development
- Human accountability in secure delivery
Module 9: Specification-Anchored Testing
- Generating test specifications from requirements
- Domain-language test design
- Safely generating test implementations
- Concepts of mutation testing
- Validating specification coverage
- Reviewing assertion strength
- Diagnostic questioning models
Module 10: System Maintenance
- Maintaining living artefacts: 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 are expected to have:
- Experience in software development projects
- A solid understanding of application architecture fundamentals
- Familiarity with APIs, backend/frontend systems, or full-stack delivery
- Basic knowledge of Agile or iterative software delivery methodologies
- An awareness of software testing concepts
- Exposure to AI coding tools is beneficial but not required
- This content is suitable for mid-level to senior technical professionals
14 Hours
