Get in Touch

Course Outline

Module 1: Context, Scope and Delivery Challenges

  • Distinguishing between autocomplete and autonomous multi-step execution
  • Common AI misconceptions in software delivery
  • Why improved prompts alone are insufficient
  • Identifying participant tooling, pain points, and goals
  • 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
  • Handling tables, diagrams, flowcharts, and mixed inputs
  • Effective management of 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
  • Validation frameworks for traceability, consistency, and completeness

Module 4: From Requirements to Code with Agentic Tools

  • The 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 versus constrained prompting approaches
  • Contract-first generation for backend and frontend components

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 handling edge cases
  • Rules documents as living governance artifacts
  • Automated gates utilizing linting and static analysis
  • Security scanning within AI-generated code
  • Dependency and architecture conformance checks
  • Human review protocols for AI outputs

Module 7: Feedback Loops and Continuous Improvement

  • Feeding 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 associated with generated code
  • Technology-specific security rules appendices
  • Pre-commit security scanning
  • 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
  • Domain-language test design
  • Safely generating test implementations
  • Concepts of mutation testing
  • Validating specification coverage
  • Reviewing assertion strength
  • Utilizing diagnostic questioning models

Module 10: Maintaining the System

  • Living artifacts: 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 should possess:

  • Hands-on experience in software development projects
  • A solid understanding of application architecture fundamentals
  • Familiarity with APIs, backend/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 beneficial but not mandatory
  • Designed for mid-level to senior technical professionals
 14 Hours

Upcoming Courses

Related Categories