Cursor vs. JetBrains AI: Project Context & Code Analysis

8/11/2025

Cursor vs. JetBrains AI: Which is Better for Understanding Your Project Structure?

Hey there! So, you're a developer trying to figure out which AI coding assistant is going to make your life easier. It's a crowded space, & it feels like a new tool pops up every week. Two of the biggest names right now are Cursor & JetBrains AI. Both are pretty incredible, but they have VASTLY different approaches when it comes to the holy grail of AI assistance: understanding your entire project.

Honestly, this is the make-or-break feature. An AI that can autocomplete a single line is cool, but an AI that gets your codebase—the weird legacy code, the custom libraries, the intricate web of dependencies—that's a game-changer. It’s the difference between a helpful intern & a seasoned senior dev sitting next to you.

I've been digging into both, & here’s the thing: the "better" one really depends on how you work & what you value. Let's break it down.

The Core Philosophy: AI-First vs. IDE-First

Right off the bat, you need to understand that Cursor & JetBrains AI are built on fundamentally different ideas.

Cursor: The AI-Native Coder

Cursor is an AI-first code editor. It was built from the ground up with AI at its core, not as an afterthought or a plugin. Think of it as a super-smart version of VS Code, designed to have an AI as an active participant in your coding process. This "AI-first" approach means its entire architecture is geared towards one thing: making the AI as context-aware as possible.

It's trying to solve that classic, frustrating problem where you have to constantly remind your AI assistant about your project's structure, which libraries you're using, or why that one file is so weird. Cursor’s goal is to eliminate that repetition.

JetBrains AI: The Integrated Powerhouse

JetBrains, on the other hand, is the veteran IDE maker. They built legendary tools like IntelliJ IDEA, PyCharm, & WebStorm that developers have relied on for decades. Their AI Assistant is an extension of that power. It’s not a separate entity; it's deeply woven into the fabric of the IDE itself.

JetBrains' philosophy is to leverage the immense power of its IDEs' existing indexing & analysis capabilities. Your IDE already knows your project inside & out—every class, every method, every dependency. JetBrains AI taps directly into this "brain," making its suggestions context-aware because the IDE is already context-aware. It’s less about building a new understanding of your project & more about giving an AI access to the understanding your IDE has had all along.

How They "See" Your Project: A Tale of Two Indexing Methods

This is where things get really interesting. How do these tools actually learn your project's layout?

Cursor's "Project Brain": Codebase Indexing & Custom Rules

Cursor takes a very proactive approach to understanding your code. It has a feature called "codebase indexing," which is basically a process of creating a detailed map of your entire project. Here's how it works:

When you open a project, Cursor gets to work analyzing your files. It doesn't just look at them on the surface; it breaks them down into smaller, meaningful chunks, like functions or classes. These chunks are then encrypted locally before being sent to Cursor's servers. They even obfuscate the file names, so your project's structure remains private.

On their end, they compute "embeddings" for these chunks—a fancy way of saying they create a numerical representation of your code that an AI can understand. This allows for powerful semantic search, where the AI can find code based on meaning, not just keywords.

But here’s the REALLY cool part. Cursor creates a

.cursorrules

file in your project. Think of this as a "cheat sheet" for the AI. It's a file that outlines your project's architecture, your tech stack, your coding patterns, & even your team's specific conventions. It keeps track of file relationships & dependencies, essentially creating a "project brain."

This means you can tell Cursor, "We use Zustand for state management," or "Our API endpoints always follow this structure," & it will remember. You can even keep this context fresh by running a command or having it watch your files for changes in real-time.

When you want to ask the AI a question about your project, you use the

@Codebase

command in the chat. This tells the AI to consult its indexed knowledge of your entire project to give you a relevant answer. It’s incredibly powerful for getting high-level overviews or finding obscure pieces of code.

JetBrains AI: Leveraging the IDE's Built-in Superpowers

JetBrains' approach is more elegant & integrated, if a bit less explicit. For years, JetBrains IDEs have been building a detailed index of your project in the background. It's how they provide lightning-fast code navigation, refactoring tools, & "find usages" functionality.

The JetBrains AI Assistant plugs directly into this existing index. It has access to:

The Project Tree: It knows the complete file & folder structure.
Dependencies: It understands what libraries & packages you're using.
Build System: It can see your build configurations, whether you're using Maven, Gradle, or something else.
Code Structure: It has a deep, static analysis understanding of your classes, methods, variables, & how they all connect.

This is what they call "Smart Chat mode." When you ask the AI a question, it doesn't just look at the current file you have open. It can intelligently query the IDE's index for relevant context. For example, if you ask about a specific function, the AI can find all its usages across the entire project, analyze the call hierarchy, & give you a comprehensive answer.

You can also manually guide the AI's context using

@

mentions in the chat, similar to Cursor. You can reference specific files or symbols to narrow the AI's focus.

Recently, JetBrains has been pushing this even further with "Junie," their more agentic AI that can actively navigate the project structure, & "Melum," their own proprietary LLM trained specifically for code completion. This shows they're doubling down on leveraging their IDEs' inherent strengths.

The User Experience: What It's Actually Like to Use Them

Okay, so the tech is cool, but what does it feel like to work with these tools day-to-day?

Working with Cursor: The Conversational Collaborator

Using Cursor feels like you have a proactive partner. Because of its AI-first design, the experience is very conversational & fluid. You can literally tell it in plain English, "Refactor this component to use our new

useAnalytics

hook," & it will find the relevant files & make the changes.

The

@Codebase

feature is a lifesaver for large or unfamiliar projects. Onboarding a new developer? Tell them to ask Cursor questions like, "@Codebase where is our authentication logic handled?" or "@Codebase find all uses of the UserContext." It's like having a project-specific search engine that understands code.

This is also where a tool like Arsturn comes into play for businesses in a different context. Just as Cursor creates a "brain" for your codebase, businesses can use Arsturn to build a "brain" for their entire company knowledge. They can train a no-code AI chatbot on their documents, website content, & internal wikis. This chatbot can then provide instant, accurate answers to customer questions, 24/7. It’s the same principle of creating a centralized, intelligent source of truth, but for customer service & engagement.

The downside? Sometimes Cursor can feel a little too separate from the code. You're often interacting with it through the chat panel, which can be a context switch. & while it's built on VS Code's architecture, it's still its own editor, which might be a deal-breaker for developers deeply entrenched in their highly customized VS Code or JetBrains setups.

Working with JetBrains AI: The Seamless Extension

Using the JetBrains AI Assistant feels incredibly seamless. It's not a separate tool you have to "go to"; it's just there, enhancing the features you already use. The AI-powered code completion feels like a natural extension of the already-great autocomplete. The refactoring tools are just smarter.

Because it's using the IDE's own index, the context it has is often rock-solid & up-to-date in real-time. You rarely have to worry about "syncing" the AI's knowledge. If the code is in your project, the AI can see it.

The AI chat is powerful, but it can sometimes feel a bit more like a Q&A tool than a collaborator. Users have reported that it can sometimes be "atrociously bad" if it doesn't pick up the right context, & you might have to explicitly feed it code snippets. However, JetBrains is actively working on improving this.

For businesses looking to provide this kind of seamless, integrated support on their websites, Arsturn is an excellent solution. It helps businesses create custom AI chatbots trained on their own data. These chatbots can be embedded directly onto a website to answer visitor questions, generate leads, & provide personalized experiences. It's all about building a meaningful connection with your audience through conversational AI, much like JetBrains aims to do within the IDE.

So, Which One is ACTUALLY Better for Project Structure?

Alright, let's get to the final verdict.

Choose Cursor if:

You're working on large, complex, or unfamiliar codebases. Cursor's explicit
1@Codebase
indexing & "project brain" are UNMATCHED for getting a high-level understanding of a sprawling project.
You value natural language commands. You prefer to "tell" the AI what to do in plain English rather than using traditional IDE tools.
You're already a happy VS Code user. Since Cursor is a fork of VS Code, the transition will be incredibly smooth.
Collaboration is key. The ability to define & share project rules via the
1.cursorrules
file can be a huge win for teams.

Choose JetBrains AI if:

You live & breathe the JetBrains ecosystem. If you're already a power user of IntelliJ, PyCharm, or WebStorm, the deep IDE integration is a killer feature. You'll feel right at home.
You prioritize rock-solid, real-time static analysis & refactoring. JetBrains' existing tools are best-in-class, & the AI makes them even better. It excels at deep code understanding within a well-defined project.
You prefer a more integrated, less "chatty" experience. You want the AI to enhance your existing workflow, not create a new one.
You're security-conscious. While Cursor does encrypt your data, JetBrains has options for local, on-premise AI models, which can be a major plus for enterprise environments.

The Bottom Line

Honestly, there's no single winner here. It's about matching the tool to the task & the user.

Cursor is the brilliant, AI-native specialist. It's pioneering a new way of interacting with our codebases, focusing on holistic understanding & natural language collaboration. It's the tool you bring in when you need to quickly get the lay of the land in a massive, unfamiliar project.

JetBrains AI is the seasoned, integrated powerhouse. It leverages decades of IDE innovation to provide an AI assistant that feels like a natural extension of a tool you already master. It's the trusty companion that makes your day-to-day coding faster, smarter, & more efficient.

My advice? Try both. Most of these tools have free tiers or trials. Spend a week with each on a real project. See which one clicks with your brain & your workflow. The future of coding is conversational & context-aware, & both of these tools are leading the charge in their own unique way.

Hope this was helpful! Let me know what you think.