JetBrains AI: Full IDE Integration

JetBrains AI is the newest leap in the evolution of developer tools, turning the familiar IntelliJ‑based IDEs into truly intelligent assistants. By weaving a large‑language model (LLM) directly into the editor, JetBrains AI offers context‑aware suggestions, on‑the‑fly code generation, and even automated testing without ever leaving the IDE. In this article we’ll explore how the integration works, walk through real‑world scenarios, and uncover pro tips that can shave hours off your development cycle.

What JetBrains AI Brings to the IDE

At its core, JetBrains AI is a service layer that sits between your codebase and the LLM, translating editor context into precise prompts and returning structured snippets. The result feels like a natural extension of the existing code‑completion engine, but with a far richer understanding of intent, project structure, and even domain‑specific libraries.

Context‑Aware Code Completion

Traditional completion suggests symbols based on static analysis. JetBrains AI adds a semantic layer: it looks at the current method, the surrounding docstring, and the imported modules to predict the next line of code. For example, typing requests. inside an async function will prompt the AI to suggest await‑compatible calls, even inserting the appropriate asyncio import if it’s missing.

import requests
import asyncio

async def fetch_user(user_id: int):
    # AI suggests the next line based on async context
    response = await requests.get(f"https://api.example.com/users/{user_id}")
    return response.json()

The snippet above demonstrates how the model anticipates the need for await and automatically adds the import for asyncio when necessary, keeping your code both correct and idiomatic.

Instant Refactoring Suggestions

Refactoring in JetBrains IDEs has always been powerful, but with AI you get suggestions that go beyond the usual rename or extract method. The AI can propose design‑level changes, such as converting a series of if‑elif statements into a match expression, or extracting a duplicated code block into a reusable utility function across multiple modules.

• Extract to function – Detects repeated logic and creates a shared helper.
• Convert to data class – Recognizes plain classes that only store attributes and suggests @dataclass conversion.
• Introduce pattern matching – Rewrites complex conditionals into match/case blocks for Python 3.10+.

When you trigger the AI refactor (Alt+Enter → “AI Refactor”), the IDE presents a preview with a concise explanation, letting you accept or tweak the suggestion before it’s applied.

AI‑Powered Test Generation

Writing unit tests is often the most tedious part of a feature rollout. JetBrains AI can generate test scaffolds from a function’s signature and docstring, automatically mocking external dependencies and asserting typical edge cases. The generated tests follow the project’s existing testing framework—be it pytest, unittest, or behave.

def calculate_discount(price: float, is_vip: bool) -> float:
    """Return the discounted price.
    VIP customers get 20% off, everyone else gets 5% off."""
    discount = 0.20 if is_vip else 0.05
    return price * (1 - discount)

After placing the cursor on the function and invoking “Generate tests with AI,” the IDE produces:

import pytest
from mymodule import calculate_discount

@pytest.mark.parametrize(
    "price,is_vip,expected",
    [
        (100.0, True, 80.0),   # VIP discount
        (100.0, False, 95.0),  # Regular discount
        (0.0, True, 0.0),      # Edge case: zero price
        (-50.0, False, -47.5)  # Edge case: negative price
    ],
)
def test_calculate_discount(price, is_vip, expected):
    assert calculate_discount(price, is_vip) == pytest.approx(expected)

The generated suite not only covers the happy path but also anticipates common edge cases, giving you a solid starting point for further refinement.

Getting Started: Enabling JetBrains AI

Before you can harness the power of AI, you need to activate the feature in your JetBrains IDE (IntelliJ IDEA, PyCharm, WebStorm, etc.). The process is straightforward and can be completed in a few clicks.

1. Open Settings/Preferences (Ctrl+Alt+S).
2. Navigate to Tools → JetBrains AI.
3. Toggle the Enable JetBrains AI switch.
4. Choose your preferred model: the default cloud‑hosted model or a self‑hosted OpenAI‑compatible endpoint.
5. Optionally configure Privacy Settings to control which files are sent to the server.
6. Click Apply and restart the IDE to finalize the activation.

Once enabled, you’ll notice new AI icons appear in the gutter and a dedicated AI Assistant tool window on the right side of the IDE. This window provides a chat‑style interface for ad‑hoc queries, such as “Explain this regex” or “Suggest a more efficient sorting algorithm.”

Practical Workflow: From Idea to Production

Let’s walk through a realistic development cycle where JetBrains AI accelerates each phase—from sketching an API to deploying a production‑ready service. The example will focus on building a small Flask microservice that validates and stores user profiles.

Step 1: Sketching the API

Start by describing the endpoint in natural language. In the AI Assistant window type:

“Create a Flask POST endpoint `/users` that accepts JSON with fields `name`, `email`, and `age`. Validate that `email` matches a standard pattern and `age` is between 13 and 120. Return a 201 response with the created user ID.”

The AI replies with a ready‑to‑paste snippet:

from flask import Flask, request, jsonify
import re
import uuid

app = Flask(__name__)

EMAIL_REGEX = re.compile(r"^[\w\.-]+@[\w\.-]+\.\w+$")

@app.route("/users", methods=["POST"])
def create_user():
    data = request.get_json()
    name = data.get("name")
    email = data.get("email")
    age = data.get("age")

    # Basic validation
    if not name or not email or age is None:
        return jsonify({"error": "Missing required fields"}), 400
    if not EMAIL_REGEX.match(email):
        return jsonify({"error": "Invalid email format"}), 400
    if not (13 <= age <= 120):
        return jsonify({"error": "Age must be between 13 and 120"}), 400

    user_id = str(uuid.uuid4())
    # In a real app, persist the user to a database here
    return jsonify({"id": user_id, "name": name, "email": email, "age": age}), 201

if __name__ == "__main__":
    app.run(debug=True)

The generated code follows PEP 8 conventions, includes a compiled regex for email validation, and uses uuid4 for unique identifiers—all without you typing a single line of boilerplate.

Step 2: Auto‑Generating Boilerplate Tests

Place the cursor on the create_user function and invoke “Generate tests with AI.” The assistant produces a pytest suite that covers happy paths, validation failures, and edge cases.

import json
import pytest
from app import app

@pytest.fixture
def client():
    app.testing = True
    with app.test_client() as client:
        yield client

def test_create_user_success(client):
    payload = {"name": "Alice", "email": "alice@example.com", "age": 30}
    response = client.post("/users", data=json.dumps(payload), content_type="application/json")
    assert response.status_code == 201
    data = response.get_json()
    assert "id" in data
    assert data["name"] == "Alice"

@pytest.mark.parametrize(
    "payload,expected_error",
    [
        ({"email": "bob@example.com", "age": 25}, "Missing required fields"),
        ({"name": "Bob", "email": "invalid-email", "age": 25}, "Invalid email format"),
        ({"name": "Bob", "email": "bob@example.com", "age": 10}, "Age must be between 13 and 120"),
    ],
)
def test_create_user_validation_errors(client, payload, expected_error):
    response = client.post("/users", data=json.dumps(payload), content_type="application/json")
    assert response.status_code == 400
    assert response.get_json()["error"] == expected_error

Notice how the AI automatically adds a client fixture, uses json.dumps for payload serialization, and parametrizes error cases for concise coverage.

Step 3: Refactoring with AI Insight

After a few iterations you realize the email regex is duplicated across multiple services. Highlight the EMAIL_REGEX line, press Alt+Enter, and select “AI Refactor → Extract shared constant.” The assistant suggests creating a new module utils/validation.py, moves the regex there, and updates all imports.

# utils/validation.py
import re

EMAIL_REGEX = re.compile(r"^[\w\.-]+@[\w\.-]+\.\w+$")

Back in app.py the AI adds:

from utils.validation import EMAIL_REGEX

This one‑click refactor eliminates manual file creation, import adjustments, and potential typos.

Real‑World Use Cases

• Legacy Code Modernization – AI can suggest migration paths from Python 2 to Python 3, automatically updating print statements, exception syntax, and library calls.
• Rapid Prototyping – Describe a feature in plain English and receive a functional prototype, complete with UI stubs for web frameworks like Django or React.
• Security Audits – Prompt the assistant to “Identify insecure deserialization patterns” and receive inline warnings plus remediation snippets.
• Documentation Generation – Generate docstrings in Google, NumPy, or Sphinx style directly from function signatures and comments.
• Cross‑Language Refactoring – Convert a Python data‑processing script into an equivalent Kotlin coroutine, preserving logic while adapting to the target language’s idioms.

Pro tip: Use the “Explain selection” command (Ctrl+Shift+Alt+E) to get a concise natural‑language summary of any complex block. This is invaluable during code reviews or when onboarding new team members.

Performance and Privacy Considerations

JetBrains AI operates in two modes: cloud‑hosted inference and on‑premise self‑hosted models. The cloud option offers the latest model updates and zero‑maintenance scaling, but it streams code snippets to JetBrains servers. If your organization has strict data‑privacy policies, you can point the IDE to a locally hosted OpenAI‑compatible endpoint, ensuring all processing stays within your firewall.

Network latency can affect suggestion latency, especially for large files. To mitigate this, the IDE caches recent prompts and batches multiple small edits into a single request. You can also adjust the Maximum Tokens setting to limit response size, balancing detail against speed.

Advanced Customization

Prompt Engineering Inside the IDE

While the default prompts are optimized for most scenarios, power users can craft custom prompts for domain‑specific tasks. Open the AI Prompt Library (found under Tools → JetBrains AI → Prompt Library) and add a new entry like:

# Prompt: Generate a FastAPI endpoint with JWT authentication
You are a senior backend engineer. Write a FastAPI POST endpoint called /login that:
1. Accepts JSON with `username` and `password`.
2. Verifies credentials against a SQLite DB.
3. Returns a signed JWT token with a 15‑minute expiry.
Include proper error handling and type hints.

Once saved, you can invoke this prompt from the editor context menu, and the AI will produce a ready‑to‑run implementation tailored to your stack.

Selecting the Underlying Model

JetBrains AI supports multiple model backends: the default JetBrains‑trained model, OpenAI’s gpt‑4o, and any OpenAI‑compatible API (e.g., Anthropic Claude, LLaMA‑2). Switching models is a matter of updating the endpoint URL and API key in the AI Settings panel. Remember that larger models provide richer explanations but consume more tokens, which can impact cost if you’re on a metered plan.

Pro tip: For code‑heavy projects, enable the “Code‑only mode” toggle. This forces the model to respond with pure code blocks, reducing token usage and eliminating unnecessary prose.

Conclusion

JetBrains AI transforms the IDE from a passive editor into an active development partner. By delivering context‑aware completions, instant refactorings, and automated test generation—all within the familiar JetBrains environment—you can focus on solving business problems instead of wrestling with boilerplate. Whether you’re modernizing legacy code, prototyping a new service, or enforcing security best practices, the integrated AI layer accelerates the entire software lifecycle. Enable it, explore the prompt library, and let the AI handle the repetitive parts while you keep the creative spark alive.