JetBrains AI Assistant Features: Code Generation, Completion, and Smart Suggestions

Mar 27, 2026

I was frustrated with AI code assistants that only understood isolated snippets of my code. I’d ask for help, get a suggestion, apply it, and then realize it broke three other parts of my codebase because the AI didn’t understand the full context.

Then I tried JetBrains AI Assistant and realized what I was missing: native IDE integration that understands my entire project, not just what I paste into a chat window.

The Problem: AI Tools Without Context

When I first started using AI coding tools, I’d constantly run into these issues:

Incomplete understanding: Copy-pasting code into a chat meant the AI missed dependencies, imports, and project structure
Hallucinated imports: The AI would suggest using libraries I didn’t have installed
Style mismatches: Suggestions didn’t match my codebase conventions
Manual context provision: I had to explain my entire project every time I asked a question

I spent more time explaining context than actually coding.

The Solution: JetBrains AI Assistant

JetBrains AI Assistant integrates directly into the IDE, giving it access to your entire codebase context. Here’s how it works and how to make the most of it.

Multi-Line Code Completion

The standout feature is multi-line code completion. Unlike traditional single-line autocomplete, this understands what you’re trying to accomplish.

Default Shortcuts (configurable in Settings → Keymap):

Multi-line completion: Tab (when suggestion appears)
Accept line: Tab
Accept word: Ctrl+→ (Windows/Linux) or ⌥→ (macOS)

I noticed the completion quality varies significantly by context. When I was writing a React component with TypeScript, the suggestions were incredibly accurate because the AI could see my:

Component props interface
State management patterns
Import statements
Related component files

Example: I started typing a function signature, and the AI completed the entire function body including proper error handling:

async function fetchUserData(userId: string): Promise<UserData> {
  try {
    const response = await apiClient.get(`/users/${userId}`);
    return response.data;
  } catch (error) {
    if (axios.isAxiosError(error)) {
      throw new ApiError(error.response?.data?.message || 'Failed to fetch user');
    }
    throw error;
  }
}

The AI knew to use apiClient because it saw my axios instance configuration elsewhere in the project.

Code Generation via AI Assistant Panel

For more complex tasks, I use the AI Assistant panel. This is where you can describe what you want in natural language.

How I use it:

1. Open AI Assistant panel (View → Tool Windows → AI Assistant)
2. Type a description like: "Create a REST API endpoint for user registration with email validation"
3. Review the generated code in the diff view
4. Accept, reject, or modify the suggestion

The key insight is that the AI can see:

My existing API patterns
Database models
Validation utilities
Error handling conventions

This means the generated code fits seamlessly into my project architecture.

Model Selection and Switching

JetBrains AI Assistant supports multiple models, and choosing the right one matters.

Available Models (varies by subscription tier):

OpenAI Codex models (default for code generation)
GPT-4 models (better for complex reasoning)
Specialized code models

How to switch models:

1. Open AI Assistant panel
2. Click the model dropdown (top of panel)
3. Choose from available models
4. Selection persists for current session

I’ve found that:

Codex models: Best for pure code completion and generation
GPT-4 models: Better for refactoring suggestions and architectural decisions
Free tier: Has limited model access, usually one or two options

Agent Permissions and Configuration

One feature I initially overlooked was Agent permissions. The AI can perform actions beyond just suggesting code.

What Agents can do:

Write and modify files
Execute terminal commands
Access project context
Perform web searches (with permission)

Configuring Permissions:

Settings → Tools → AI Assistant → Agent Settings

Permissions:
✓ Read project files
✓ Write to project files
✗ Execute terminal commands (disabled by default)
✓ Search web for documentation

I keep terminal command execution disabled unless I’m working in a controlled environment. The AI once tried to run npm install when I already had dependencies installed, which would have wasted time.

Context-Aware Features

The real power comes from context awareness. Here’s what the AI can see:

Open files: Currently active editor tabs
Recent changes: Git history and recent edits
Project structure: File hierarchy and relationships
Dependencies: package.json, requirements.txt, etc.
Code conventions: Naming patterns, import styles

Example of context in action:

I was working on a Spring Boot application and asked for a repository method. The AI generated:

public interface UserRepository extends JpaRepository&lt;User, Long&gt; {
    @Query("SELECT u FROM User u WHERE u.email = :email AND u.active = true")
    Optional&lt;User&gt; findActiveByEmail(@Param("email") String email);

    @Modifying
    @Query("UPDATE User u SET u.lastLoginAt = :timestamp WHERE u.id = :userId")
    int updateLastLogin(@Param("userId") Long userId, @Param("timestamp") LocalDateTime timestamp);
}

It knew:

I was using Spring Data JPA (from dependencies)
My entity naming convention (singular, not plural)
I prefer Optional return types for single results
I use @Param annotations (from other repositories)

Common Mistakes to Avoid

After using JetBrains AI Assistant for several months, here are mistakes I made:

Mistake 1: Not Exploring Model Options

I stuck with the default model for weeks. Once I tried other models, I found significant quality differences for specific tasks.

Mistake 2: Ignoring Keyboard Shortcuts

I initially used only the panel for everything. Learning the shortcuts for inline completion dramatically increased my productivity.

Mistake 3: Over-Trusting Generated Code

I applied suggestions without reviewing them. Always use the diff view to understand what changed.

Mistake 4: Missing Context

The AI only sees what’s accessible in the IDE. If I have configuration in environment variables or external systems, I need to provide that context explicitly.

Integration with JetBrains Ecosystem

The AI Assistant works across all JetBrains IDEs:

IntelliJ IDEA: Java, Kotlin, Spring Boot
PyCharm: Python, Django, Flask
WebStorm: JavaScript, TypeScript, React, Vue
GoLand: Go
Rider: C#, .NET

The integration depth varies. IntelliJ IDEA and PyCharm have the most mature integrations because JetBrains has more data on patterns in these ecosystems.

Performance Considerations

I’ve noticed the AI can slow down the IDE, especially with:

Large codebases (10,000+ files)
Projects with many dependencies
Complex type systems

Optimization tips:

Settings → Tools → AI Assistant → Performance

✓ Enable file-based context limit (max files: 100)
✓ Cache project context between sessions
✗ Disable real-time suggestions for large files (>1000 lines)

Comparison with Alternatives

I’ve tried several alternatives:

Feature	JetBrains AI	GitHub Copilot	Cursor
IDE Integration	Native	Plugin	Standalone IDE
Context Awareness	Full project	Open files	Full project
Model Selection	Yes	No	Yes
Offline Mode	No	No	No
Pricing	Subscription	Subscription	Freemium

The native integration is JetBrains AI’s biggest advantage. It feels like a natural extension of the IDE rather than a bolted-on feature.

When to Use Which Feature

I’ve developed a mental model for when to use each feature:

Inline Completion: Quick functions, variable names, simple logic AI Panel: Complex features, refactoring, explanations Code Generation: Boilerplate, repetitive patterns Chat: Questions about codebase, debugging help

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!