Role-Based Programme
RB0611

JetBrains AI Assistant for Product & Service Management

Understand Code, Validate Features & Accelerate Product–Engineering Collaboration

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

Programme Objectives

  • Develop functional proficiency in using JetBrains AI Assistant to understand applications, features, technical dependencies, and codebase context.
  • Translate product requirements, user stories, acceptance criteria, and business rules into clearer engineering discussions.
  • Use AI-assisted code explanation, issue investigation, documentation, test generation, and change summaries to support feature validation.
  • Improve collaboration between product managers, developers, QA teams, architects, and service stakeholders.
  • Apply responsible AI practices for source-code access, sensitive information, generated recommendations, and human technical validation.

Tools covered

JetBrains AI AssistantAI ChatCodebase ModeAI ActionsExplain CodeFind ProblemsGenerate DocumentationGenerate Unit TestsVCS AI AssistanceCoding AgentsProject Rules`.aiignore`

Who should attend

  • Product Managers
  • Technical Product Managers
  • Product Owners
  • Digital Product Managers
  • Service Managers
  • Product Operations Professionals
  • Product Analysts
  • Business Analysts
  • Technical Business Analysts
  • Application Product Owners
  • Platform Product Managers
  • Product & Service Team Leads

Prerequisites & Participant Readiness

  • Basic understanding of product or service-management practices
  • Familiarity with requirements, user stories, acceptance criteria, defects, or release processes
  • Basic understanding of software-development terminology
  • Ability to read simple code or technical documentation is beneficial
  • Familiarity with Git, APIs, databases, or application architecture is helpful
  • No advanced software-development expertise required
  • Access to a supported JetBrains IDE with AI Assistant is recommended for hands-on activities

TOC Modules

Concepts
  • Understanding AI Assistant within JetBrains development environments
  • Exploring AI Chat, contextual assistance, AI Actions, and coding-agent concepts
  • Identifying product-management use cases across discovery, delivery, testing, and release
  • Differentiating AI-generated technical guidance from verified engineering decisions
Practical activities
  • Exploring AI Chat inside a sample application project
  • Asking questions about application functionality
  • Reviewing files and project context used by AI Assistant
  • Building Product Question → Code Context → AI Explanation → Team Validation workflow

Scenarios

New Feature Requirement to Engineering Readiness

Product Requirement → User Story → AI Codebase Analysis → Dependency Identification → Refined Acceptance Criteria → Agent / Engineering Plan → Product Review

Participants evaluate a proposed feature against an existing application, identify impacted technical areas, refine acceptance criteria, and prepare a more informed engineering handoff.

Customer-Reported Issue to Product Decision

Customer Issue → Reproduction Evidence → Logs / Code Context → AI-Assisted Investigation → Related Tests & Recent Changes → Engineering Validation → Product Action

Participants investigate a customer-impacting issue using available technical context and convert validated findings into a clear product decision, defect update, or release action.

## Current Capability Reference

JetBrains AI Assistant 2026.2 provides **context-aware AI Chat** that can automatically collect relevant project context through Codebase Mode or accept explicitly attached files, folders, symbols, commits, images, and other project elements. This makes it useful for product professionals who need to investigate how a feature relates to an existing application without manually searching every file.

Current AI Assistant capabilities include **Explain Code, Find Problems, in-editor code generation, code completion, documentation generation, and unit-test generation**. Generated changes and tests remain reviewable before being accepted into the project.

AI Assistant is also integrated with version-control workflows and can **explain commits, generate commit messages, create pull/merge-request titles and descriptions, and summarize incoming pull requests**. These capabilities are particularly useful for product managers reviewing implementation progress and preparing release-level communication.

JetBrains AI Assistant now provides access to multiple **coding agents**, including JetBrains Junie and supported third-party agents, while project instruction files and Project Rules can communicate architecture, conventions, workflows, restrictions, and definitions of done.

For governance, JetBrains allows organizations to restrict AI Assistant at project level or exclude specific files and folders through **`.aiignore`**. JetBrains also notes that AI requests can include selected code and contextual project information sent to the configured model provider, making organizational data-handling and source-code policies important considerations.

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