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Duration 14 hours
Course Outline
Context, Scope, and Delivery Challenges
- Autocomplete versus autonomous multi-step execution
- Common AI misconceptions in software delivery
- Why enhanced prompting alone is insufficient
- Identifying participant tooling, pain points, and strategic goals
- Selecting the optimal AI operating model for engineering teams
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 media inputs
- Effective management of context windows
Boundaries of Human Judgment
- Areas where human decision-making remains critical
- Identifying hallucinated dependencies
- Detecting fabricated constraints and inverted logic
- Preventing unsafe default behaviors
- Validation frameworks for traceability, consistency, and completeness
From Requirements to Code Using Agentic Tools
- Architecture-first delivery models
- Component mapping and service boundary definition
- API contracts as delivery anchors
- Implementing persistent rules and constraints in AI tools
- Linking task instructions directly to requirements
- Comparing minimal prompting versus constrained prompting approaches
- Contract-first generation of backend and frontend code
The Agentic Iteration Loop
- The self-correction spiral
- Controlled iterative delivery cycles
- Reviewing diffs and code modifications
- Detecting scope creep and unauthorized changes
- Managing limited context memory
- Leveraging iteration history for continuous improvement
Enforcing Code Quality
- Applying prompt constraints for edge cases
- Utilizing rules documents as living governance artifacts
- Automated gates using linting and static analysis
- Security scanning of AI-generated code
- Checks for dependency and architecture compliance
- Human review protocols for AI outputs
Feedback Loops and Continuous Improvement
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- Integrating structured failures back into AI workflows
- Defining bounded iterations and stop criteria
- Logging cycles and outcomes
- Evolving rules documents over time
- Building reusable engineering intelligence
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 processes
Testing Aligned with Specifications
- Deriving test specifications from requirements
- Domain-language based test design
- Safely generating test implementations
- Concepts of mutation testing
- Validating specification coverage
- Reviewing assertion strength
- Using diagnostic questioning models
System Maintenance
- Living artifacts: contracts, maps, rules, and test specifications
- Evolving constraints over time
- AI governance for long-term maintainability
- Preventing technical debt through AI controls
- Operating models for sustainable AI engineering teams
Requirements
Participants are expected to possess:
- Practical experience in software development projects
- A solid grasp of fundamental application architecture principles
- Familiarity with APIs, backend/frontend systems, or full-stack delivery models
- Basic understanding of Agile or iterative software delivery methodologies
- Awareness of core software testing concepts
- Exposure to AI coding tools is beneficial but not required
- Ideal for mid-level to senior technical professionals
