Role-Based Programme
RB0601

JetBrains AI Assistant for Information Technology

AI-Assisted Coding, Troubleshooting & Developer Productivity

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

Programme Objectives

  • Understand how JetBrains AI Assistant supports software development, application maintenance, troubleshooting, testing, and technical documentation.
  • Explore project-aware AI Chat, code explanation, code generation, completion, and error-investigation capabilities.
  • Experience using structured prompts and relevant project context to improve technical AI responses.
  • Discover practical applications for coding, debugging, testing, and routine developer tasks.
  • Recognise the importance of code review, testing, security, confidentiality, and human technical validation.

Tools covered

JetBrains AI AssistantAI ChatProject ContextCloud Code CompletionGenerate CodeExplain CodeExplain Runtime ErrorsGenerate Unit TestsAI ActionsCoding Agents

Who should attend

  • IT Managers
  • Software Developers
  • Application Developers
  • Full-Stack Developers
  • Front-End Developers
  • Backend Developers
  • Technical Support Engineers
  • DevOps Professionals
  • Cloud Engineers
  • Solution Architects
  • Application Architects
  • Technical Leads

Prerequisites & Participant Readiness

  • Basic understanding of software-development concepts
  • Familiarity with source code, applications, or development workflows
  • Basic programming awareness is recommended for hands-on exercises
  • Advanced AI knowledge is not required
  • No previous JetBrains AI Assistant experience required
  • Hands-on activities require a supported JetBrains IDE with AI Assistant enabled

TOC Modules

Concepts
  • Understanding AI Assistant within JetBrains IDE development workflows
  • Exploring AI Chat, models, agents, project context, and AI Actions
  • Mapping AI assistance across Understand → Build → Test → Troubleshoot
  • Understanding the difference between Chat suggestions and agent-executed development tasks
Practical activities
  • Exploring AI Chat inside a sample software project
  • Attaching relevant files or symbols as project context
  • Asking AI Assistant to summarise the purpose of a selected application component

Scenarios

Application Requirement to Tested Feature

IT Requirement → Project Context → AI Prompt → Generated Code → Developer Review → Unit Tests → Execution → Validation

Participants use JetBrains AI Assistant to convert a small application requirement into reviewed and tested code while maintaining developer ownership of implementation quality.

Runtime Error to Verified Resolution

Application Failure → Error Evidence → AI Explanation → Suggested Fix → Developer Review → Retest → Regression Check → Resolution

Participants investigate a realistic application error with AI assistance and validate the proposed resolution through structured technical review and testing.

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