Tool or Technology
TT0084

Mastering JetBrains AI Assistant

Advanced AI Coding, Agents & Intelligent Development Workflows

JetBrains AI Assistant
Duration
32 Hours
Level
Advanced
Delivery
Instructor-Led
Format
Capability Development

Programme Objectives

  • Develop advanced expertise in using JetBrains AI Assistant for code generation, codebase understanding, refactoring, debugging, testing, documentation, and software delivery.
  • Apply AI Chat and integrated coding agents to plan and execute complex, multi-file development activities using relevant IDE and repository context.
  • Build reusable AI-development workflows using Project Rules, agent instructions, Skills, Prompt Library, MCP integrations, and external development tools.
  • Configure appropriate cloud, third-party, and local AI models based on development requirements, privacy, performance, and organisational policies.
  • Establish secure and governed AI-assisted engineering practices covering source-code access, tool execution, self-review, validation, permissions, and human oversight.

Technology covered

JetBrains AI AssistantAI ChatJetBrains PickJunieJunie LocalClaude AgentCodexGitHub Copilot AgentAgent Client Protocol (ACP)Model Context Protocol (MCP)Project RulesAGENTS.mdAgent SkillsPrompt LibraryAI Self-ReviewLocal & Third-Party Models

Who should attend

  • Software Developers & Application Engineers
  • Java, Kotlin, Python, JavaScript & .NET Developers
  • Full-Stack Developers
  • Frontend & Backend Developers
  • Technical Leads & Engineering Leads
  • Software Architects & Solution Architects
  • DevOps & Platform Engineers
  • QA & Test Automation Engineers
  • Database Developers & Data Engineers
  • Engineering Managers
  • Application Modernisation Teams
  • Developer Productivity Teams
  • Enterprise Engineering Teams
  • AI-Assisted Development Teams
  • Technology & Innovation Professionals

Prerequisites & Participant Readiness

  • Working knowledge of at least one programming language
  • Familiarity with a JetBrains IDE such as IntelliJ IDEA, PyCharm, WebStorm, Rider, GoLand, PhpStorm, or related IDE
  • Basic knowledge of software-development, debugging, testing, and version-control practices
  • Familiarity with Git and source-code repositories is recommended
  • Basic command-line knowledge is beneficial
  • Familiarity with APIs and external development tools is helpful for MCP and agent-integration modules
  • No previous JetBrains AI Assistant course attendance is required
  • Availability of individual agents, models, and AI capabilities may depend on IDE, subscription, provider account, operating system, and organisational configuration

TOC Modules

Concepts
  • Understanding JetBrains AI Assistant as an integrated AI-development environment
  • Understanding AI Chat, code completion, AI Actions, agents, and contextual assistance
  • Differentiating conversational AI, inline assistance, and autonomous coding agents
  • Understanding how IDE context improves AI-generated responses
  • Understanding limitations of AI-generated code and developer accountability
Practical activities
  • Exploring AI Assistant across realistic development activities
  • Comparing manual, AI-assisted, and agent-driven development workflows
  • Identifying high-value AI opportunities across the software-development lifecycle

Scenarios

Requirement-to-Reviewed Feature Development

Business Requirement → Project Context → AI Planning → Junie / Coding Agent → Multi-File Implementation → Testing → Debugging → AI Self-Review → Human Review → Commit

Participants create an advanced AI-assisted development workflow that converts a structured requirement into implemented, tested, documented, and review-ready code while maintaining engineering standards and developer oversight.

Secure Enterprise AI Development Environment

Enterprise Codebase → Project Rules + AGENTS.md + .aiignore → Approved AI Model / Local Model → MCP Tools → Agent Execution → Automated Tests → Self-Review → Governance Validation

Participants configure a controlled AI-development environment that standardises agent behaviour, restricts sensitive context, integrates approved development tools, and applies repeatable quality and governance controls.

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