AI Assisted Software Delivery Training Course
This advanced, hands-on seminar is tailored for software engineers, architects, technical leads, engineering managers, and delivery teams seeking to leverage Artificial Intelligence to accelerate the entire software delivery lifecycle, spanning from initial requirements analysis through to production-ready code.
The programme emphasizes the structured, controlled, and enterprise-grade application of AI and agentic tools. Participants move beyond basic prompting techniques to learn how to transform stakeholder specifications into architecture designs, API contracts, source code, test suites, and governed delivery workflows.
Special emphasis is placed on maintaining human oversight, ensuring quality control, achieving traceability, upholding security standards, conducting rigorous testing, and employing safe iteration models. This approach ensures that AI serves as a productivity multiplier rather than introducing new risks.
Through practical demonstrations, real-world case studies, and structured exercises, participants will acquire a robust framework for utilizing AI responsibly within modern software engineering environments.
This course is available as onsite live training in Kenya or online live training.Course Outline
Module 1: Context, Scope, and Delivery Challenges
- Distinguishing between autocomplete and autonomous multi-step execution
- Addressing common AI misconceptions in software delivery
- Understanding 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
- Employing requirement extraction techniques
- Implementing chunking strategies: structural, semantic, and sliding-window methods
- Preserving dependencies and cross-references
- Handling tables, diagrams, flowcharts, and mixed input formats
- Effectively managing 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
- Applying validation frameworks for traceability, consistency, and completeness
Module 4: From Requirements to Code with Agentic Tools
- Adopting an architecture-first delivery model
- Mapping components and defining service boundaries
- Leveraging API contracts as delivery anchors
- Applying persistent rules and constraints within AI tools
- Linking task instructions to specific requirements
- Comparing minimal prompting versus constrained prompting approaches
- Generating backend and frontend code using a contract-first approach
Module 5: Agentic Iteration Loop
- Navigating the self-correction spiral
- Executing 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
- Defining prompt constraints for edge cases
- Treating rules documents as living governance artifacts
- Implementing automated gates with 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
- Refining rules documents over time
- Building reusable engineering intelligence
Module 8: Security Anti-Patterns in AI Delivery
- Identifying common security risks in generated code
- Reviewing technology-specific security rule appendices
- Conducting pre-commit security scanning
- Implementing secure SDLC controls for AI-assisted development
- Ensuring human accountability in secure delivery
Module 9: Testing Anchored to Specifications
- Generating test specifications 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
- Applying AI governance for long-term maintainability
- Preventing technical debt using AI controls
- Establishing an operating model for sustainable AI engineering teams
Requirements
Participants should possess the following:
- Practical experience in software development projects
- A solid understanding of fundamental application architecture principles
- Familiarity with APIs, backend or 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 advantageous but not mandatory
- Suitable for mid-level to senior technical professionals
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
AI Assisted Software Delivery Training Course - Enquiry
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