Role-Based Programme
RB0602

JetBrains AI Assistant for Information Technology

AI-Assisted Coding, Troubleshooting & IT Automation

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Duration
8 Hours
Level
Basic
Delivery
Instructor-Led
Format
Workshop

Programme Objectives

  • Build foundational capability in using JetBrains AI Assistant for application development, scripting, troubleshooting, and technical productivity.
  • Use project-aware AI assistance to understand unfamiliar codebases, generate and modify code, and investigate technical issues.
  • Apply AI-assisted workflows to testing, refactoring, documentation, Git activities, and routine IT automation.
  • Develop practical methods for collaborating with coding agents while maintaining technical review and approval.
  • Apply source-code security, confidentiality, testing, and human-validation practices when using AI-generated technical outputs.

Tools covered

JetBrains AI AssistantAI ChatProject ContextCode ExplanationCode GenerationCode CompletionRefactoring AssistanceTest GenerationDocumentation AssistanceCommit ContextJunie Coding AgentProject InstructionsMCP Concepts

Who should attend

  • IT Managers
  • Software Developers
  • Application Developers
  • Full-Stack Developers
  • Frontend Developers
  • Backend Developers
  • Application Support Engineers
  • System Engineers
  • DevOps Engineers
  • IT Automation Professionals
  • Technical Consultants
  • Solution Engineers
  • Platform Engineers
  • IT Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of software-development or IT-support activities
  • Familiarity with at least one programming or scripting language is recommended
  • Basic knowledge of files, applications, APIs, command-line tools, or Git is helpful
  • Access to a supported JetBrains IDE is recommended
  • No previous JetBrains AI Assistant experience required

TOC Modules

Concepts
  • Understanding JetBrains AI Assistant within modern IDE-based development workflows
  • Understanding AI Chat, editor assistance, project context, and agent-based workflows
  • Identifying practical applications across coding, troubleshooting, support, automation, and documentation
  • Recognising where AI-generated technical output requires developer or administrator validation
Practical activities
  • Exploring AI Assistant within a sample application project
  • Asking project-aware questions about application structure and functionality
  • Creating a technical summary of an unfamiliar codebase

Scenarios

Application Change to Tested Release

Technical Requirement → Project Context Analysis → AI-Assisted Code Generation → Developer Review → Test Generation → Defect Fixing → Regression Validation → Documentation → Release

Participants use JetBrains AI Assistant to implement a controlled application change while maintaining human review, testing, and release accountability.

Production Issue to Preventive Automation

Operational Alert → Logs & Error Evidence → AI-Assisted Investigation → Root-Cause Confirmation → Corrective Fix → Validation → Automation Script → Preventive Control

Participants investigate a realistic application issue and convert the learning into a reusable IT automation or preventive technical workflow.

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