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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
- Why superior 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 requirement extraction
- Chunking strategies: structural, semantic, and sliding-window approaches
- Preserving dependencies and cross-references
- Working effectively with tables, diagrams, flowcharts, and mixed inputs
- Efficiently managing context windows
Module 3: Human Judgment Boundaries
- Identifying areas where human decision-making remains critical
- Detecting hallucinated dependencies
- Spotting fabricated constraints and inverted logic
- Preventing unsafe, overly helpful defaults
- Applying validation frameworks for traceability, consistency, and completeness
Module 4: From Requirements to Code with Agentic Tools
- Adopting an architecture-first delivery model
- Component mapping and defining service boundaries
- Utilizing API contracts as delivery anchors
- Implementing persistent rules and constraints within AI tools
- Linking task instructions to specific requirements
- Comparing minimal prompting versus constrained prompting approaches
- Executing contract-first backend and frontend generation
Module 5: Agentic Iteration Loop
- Navigating the self-correction spiral
- Managing controlled iterative delivery cycles
- Reviewing diffs and code changes effectively
- Identifying scope creep and unauthorized modifications
- Managing limited context memory constraints
- Leveraging iteration history for continuous improvement
Module 6: Code Quality Enforcement
- Applying prompt constraints to handle edge cases
- Treating rules documents as living governance artifacts
- Implementing automated gates via linting and static analysis
- Conducting security scanning on AI-generated code
- Verifying dependency and architecture conformance
- Establishing 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 for analysis
- Refining rules documents over time
- Building reusable engineering intelligence
Module 8: Security Anti-Patterns in AI Delivery
- Understanding common security risks in generated code
- Reviewing technology-specific security rule 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 domain-language tests
- Safely generating test implementations
- Understanding mutation testing concepts
- Validating specification coverage
- Reviewing assertion strength
- Utilizing diagnostic questioning models
Module 10: Maintaining the System
- Maintaining living artifacts: contracts, maps, rules, and test specs
- Evolving constraints over time
- Establishing 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:
- Hands-on experience in software development projects
- A solid understanding of fundamental application architecture concepts
- 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 principles
- Exposure to AI coding tools is beneficial but not mandatory
- The course is suitable for mid-level to senior technical professionals
14 Hours
