Role-Based Programme
RB1480

AI-Powered Software Development & Engineering

Smarter Coding, Debugging & Development Productivity

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Duration
4 Hours
Level
Awareness
Delivery
Instructor-Led
Format
Awareness Session

Programme Objectives

  • Understand how AI can support Software Development and Engineering across coding, debugging, documentation, testing, and code review.
  • Explore practical prompting techniques for generating code, explaining logic, identifying defects, refactoring, and creating technical documentation.
  • Apply AI to accelerate common development tasks while improving clarity, consistency, and developer productivity.
  • Identify opportunities to improve development speed, troubleshooting, code understanding, and documentation quality.
  • Recognise source-code security, intellectual property, technical validation, testing, and human-review responsibilities when using AI.

Tools covered

Generative AI AssistantsAI-Assisted CodingCode ExplanationDebugging SupportRefactoring AssistanceTechnical DocumentationTest SupportBasic Development Workflow Automation

Who should attend

  • Software Developers
  • Software Engineers
  • Application Developers
  • Frontend Developers
  • Backend Developers
  • Full-Stack Developers
  • Web Developers
  • Mobile Application Developers
  • Technical Leads
  • Development Engineers
  • Software Architects
  • Application Engineers
  • Software Development Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of software development or programming concepts
  • Familiarity with source code, applications, APIs, or development workflows is helpful
  • Basic computer and technical skills
  • No advanced AI or machine-learning knowledge required
  • No previous Generative AI experience required

TOC Modules

Concepts
  • Understanding Generative AI and its relevance to modern software-development workflows
  • Identifying AI applications across coding, debugging, testing, documentation, and code review
  • Understanding AI assistance versus Software Engineer judgement and accountability
  • Recognising security, quality, and production-sensitive activities requiring human validation
Practical activities
  • Mapping common development activities to potential AI applications
  • Comparing a traditional development task with an AI-assisted approach

Scenarios

Business Requirement to Working Code Draft

Requirement → AI-Assisted Interpretation → Logic Design → Code Draft → Review → Test Cases → Refined Implementation

Participants use AI to convert a sample software requirement into a draft implementation, review the generated logic, create basic test scenarios, and refine the output before developer validation.

Defect Report to Debugging & Resolution Summary

Defect Report → Error Context → AI-Assisted Analysis → Possible Causes → Debugging Steps → Code Fix Proposal → Validation → Resolution Note

Participants use AI to organise a sample software defect, analyse possible causes, develop debugging steps, and prepare a structured resolution summary while retaining final code changes and production decisions with authorised development teams.

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