Role-Based Programme
RB0603

JetBrains AI Assistant for Information Technology

AI-Assisted Software Development, Testing & Engineering Workflows

IMAGE REQUIRED
Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Develop functional proficiency in using JetBrains AI Assistant across software development, maintenance, testing, debugging, and technical documentation.
  • Apply project-aware AI assistance to understand codebases, generate and refactor code, diagnose issues, and accelerate routine engineering tasks.
  • Use AI-assisted testing, code review, Git, pull-request, and change-analysis workflows to improve software-delivery quality.
  • Explore coding agents and MCP-enabled workflows for controlled multi-step engineering automation.
  • Establish secure and governed AI-development practices covering source-code access, project rules, sensitive information, human review, and production readiness.

Tools covered

JetBrains AI AssistantAI ChatAI ActionsCode GenerationExplain CodeFind ProblemsAI RefactoringGenerate Unit TestsSelf-Review with AIVCS AI AssistanceCoding AgentsProject Rules`.aiignore`JetBrains MCP Server

Who should attend

  • Software Engineers
  • Application Developers
  • Full-Stack Developers
  • Frontend Developers
  • Backend Developers
  • IT Application Professionals
  • Technical Leads
  • Solution Architects
  • DevOps Engineers
  • Platform Engineers
  • QA Automation Engineers
  • Application Support Engineers
  • IT Development Managers
  • Digital Transformation Technology Teams

Prerequisites & Participant Readiness

  • Working knowledge of software-development concepts
  • Familiarity with at least one programming language supported by a JetBrains IDE
  • Basic understanding of application architecture and debugging
  • Familiarity with Git or another version-control system is recommended
  • Basic understanding of software testing is helpful
  • Access to a supported JetBrains IDE and AI Assistant is recommended for hands-on activities

TOC Modules

Concepts
  • Understanding AI Assistant inside the JetBrains IDE ecosystem
  • Exploring AI Chat, AI Actions, models, agents, and contextual assistance
  • Understanding project-aware AI interactions using files, folders, symbols, commits, and other context
  • Differentiating conversational assistance from autonomous agent execution
Practical activities
  • Exploring AI Chat within a sample software project
  • Attaching targeted project context to a technical question
  • Asking AI Assistant to explain project structure
  • Building Technical Question → Project Context → AI Response → Engineer Validation workflow

Scenarios

Application Feature from Requirement to Pull Request

Technical Requirement → AI Chat Context → Code Generation → Refactoring → Unit Tests → Find Problems → AI Self-Review → Commit Summary → Pull Request

Participants implement a sample application feature and use JetBrains AI Assistant throughout coding, validation, testing, review, and version-control preparation.

Legacy Module Investigation & Modernization

Existing Codebase → Explain Code → Dependency Understanding → Problem Analysis → Refactoring Plan → Coding Agent → Tests → Human Review

Participants analyze an unfamiliar or legacy application component, identify improvement opportunities, and create a controlled modernization workflow while preserving engineering oversight.

## Current Capability Reference

JetBrains **AI Assistant 2026.2** currently provides AI Chat as the primary interface for working with both language models and coding agents. Developers can attach **files, folders, symbols, images, commits, and other project elements** as context, while responses can include code, terminal commands, file edits, and technical explanations.

Current in-editor capabilities include **code generation, Explain Code, Find Problems, AI-assisted refactoring, and unit-test generation**. Generated code and tests remain reviewable before developers accept the changes into the project.

JetBrains' VCS integration currently supports **AI-generated commit messages, Self-Review with AI, commit explanations, pull/merge-request titles and descriptions, incoming pull-request summaries, and AI-assisted Git conflict resolution**.

AI Assistant also supports integrated **coding agents** that can plan multi-step tasks, edit multiple files, run commands and tests, and use external tools. JetBrains currently lists integrated agents such as **Junie**, while its agent framework also supports configurable instructions and MCP tools; developers can review or roll back resulting changes.

For development governance, **Project Rules** can automatically or selectively apply project-specific coding standards and architecture guidance, while `.aiignore` can prevent AI Assistant from processing specified files and folders. JetBrains also notes that prompts and relevant pieces of source code may be sent to the configured model provider, so organizational data-handling policies remain important.

JetBrains IDEs also include an integrated **MCP Server**, enabling approved external AI development clients to access IDE capabilities such as project context, terminal operations, and version-control tools. Because MCP-connected clients can receive powerful project access, permission configuration and human oversight should form part of enterprise deployment practices.

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