Tool or Technology
TT0083

JetBrains AI Assistant in Action

Code, Refactor & Build Smarter Development Workflows

JetBrains AI Assistant
Duration
16 Hours
Level
Intermediate
Delivery
Instructor-Led
Format
Capability Training

Programme Objectives

  • Develop functional proficiency in using JetBrains AI Assistant for code generation, codebase understanding, refactoring, debugging, testing, documentation, and development productivity.
  • Apply structured prompting and project-context techniques to improve the relevance, accuracy, and maintainability of AI-generated development outputs.
  • Use AI Chat, inline completion, Next Edit Suggestions, AI Actions, and coding agents across realistic software-development workflows.
  • Build reusable AI-assisted engineering practices using Prompt Library, Project Rules, agent instructions, local or third-party models, and MCP-connected tools.
  • Apply appropriate testing, security, code review, privacy, and developer-validation practices when using AI within professional development environments.

Technology covered

JetBrains AI AssistantAI ChatAI Code CompletionNext Edit SuggestionsAI ActionsPrompt LibraryProject RulesAI Self-ReviewCoding AgentsJunieLocal & Third-Party ModelsModel Context Protocol (MCP)

Who should attend

  • Software Developers
  • Software Engineers
  • Java & Kotlin Developers
  • Python Developers
  • .NET Developers
  • Frontend & Backend Developers
  • Full-Stack Developers
  • QA & Test Automation Engineers
  • DevOps & Platform Engineers
  • Application Support Engineers
  • Technical Leads
  • Solution Architects

Prerequisites & Participant Readiness

  • Basic programming proficiency in at least one supported programming language
  • Familiarity with functions, classes, application logic, APIs, and source-code structures
  • Basic experience using a JetBrains IDE such as IntelliJ IDEA, PyCharm, WebStorm, Rider, or another supported IDE
  • Basic understanding of Git and source-control concepts is recommended
  • Familiarity with debugging and software-testing practices is beneficial
  • No previous JetBrains AI Assistant training is required
  • Access to specific models, agents, or AI features may depend on organisational configuration and available subscriptions

TOC Modules

Concepts
  • Understanding Generative AI in modern software-development workflows
  • Understanding JetBrains AI Assistant within JetBrains IDEs
  • Exploring AI Chat, code completion, AI Actions, and coding agents
  • Understanding cloud, third-party, and local model options
  • Understanding AI limitations and developer accountability
Practical activities
  • Configuring AI Assistant within a JetBrains IDE
  • Exploring common AI-assisted coding activities
  • Comparing traditional and AI-assisted development approaches

Scenarios

Requirement to Tested Application Feature

Business Requirement → Project Context → AI Chat → Implementation → AI Code Completion → Refactoring → Unit Tests → AI Self-Review → Developer Approval

Participants use JetBrains AI Assistant throughout a realistic feature-development lifecycle to understand an existing application, implement functionality, improve code quality, generate tests, and prepare the change for human-reviewed integration.

Legacy Defect to Verified Resolution

Defect Report → Codebase Investigation → Logs / Stack Trace → Root-Cause Analysis → AI-Assisted Fix → Regression Tests → Code Review → Verified Resolution

Participants use JetBrains AI Assistant to investigate an existing software defect, locate relevant code, identify the underlying cause, implement a controlled fix, and validate that the correction has not introduced regressions.

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