JetBrains AI Assistant - The Only AI That Gets My Weird Codebase

Been using JetBrains AI Assistant since it launched in December 2023 and it's the first AI tool that doesn't make me want to chuck my ThinkPad out the office window. Unlike GitHub Copilot which suggests random shit from Stack Overflow, JetBrains AI actually understands your project structure and doesn't try to implement JWT authentication with fucking jQuery.

Here's what makes it different: it reads your entire codebase, not just the current file. When I ask it to generate a REST endpoint for our Spring Boot 3.2 project, it knows we're using custom security annotations and OpenAPI 3 documentation. Copilot would suggest deprecated Spring Security patterns that stopped working in 2022.

The Credit System Is Annoying But At Least It's Honest

August 2025, JetBrains switched to this credit-based bullshit where each credit costs a buck. Yeah, watching that meter tick down sucks, but at least they tell you upfront what you're paying instead of those AWS billing surprises that make you question your life choices. I burned through 23 credits debugging a race condition in our payment service last Tuesday - expensive, but way faster than reading logs for 3 hours.

The AI connects to GPT-4, Claude 3.5, and Gemini, so you're not stuck with whatever model JetBrains picked. Plus they added local model support if you're paranoid about sending your code to the cloud.

Actually Useful Features (Not Marketing Fluff)

The AI Chat is where it gets interesting. I can paste an error like java.util.concurrent.RejectedExecutionException and get actual solutions that work with our custom thread pool configuration, not generic advice about increasing heap size.

Code completion works way better than IntelliJ's built-in stuff. When I start typing a method that handles JWT token validation, it generates the whole thing including proper exception handling for expired tokens and signature verification. The unit test generation actually creates useful tests with proper mocks instead of assertions that test nothing.

What Actually Works vs What Makes Me Want to Quit Programming

The code explanations work great for that legacy authentication module nobody wants to touch. I threw 800 lines of uncommented JWT validation at it last month and it actually figured out what the original dev was trying to do. Saved me from having to reverse-engineer that clusterfuck.

Commit message generation is surprisingly good - instead of my usual "fix stuff" it generates proper conventional commit messages. Though it did once suggest "feat: add null check" for fixing a critical production bug, which... yeah, that's totally a new feature, you absolute muppet.

Refactoring suggestions usually don't break your build, unlike that time I let ChatGPT "improve" our service layer and it broke dependency injection in ways I didn't know were possible.

Here's where it completely shits the bed:

Context limits hit hard when you're working with our 500k line monorepo. I'll be deep in a debugging session and suddenly it forgets we were talking about the payment service. Cool, now I get to explain our entire architecture again.

Peak hour performance is like trying to debug through molasses. 3pm EST? Good luck getting a response under 30 seconds. Perfect fucking timing when your staging environment is on fire and the client is screaming about their broken checkout flow.

And that Version 2025.2 bug where it forgets conversation context mid-debugging? Still not fixed. I've filed three support tickets. They keep telling me to "start a fresh chat" - yeah, that's exactly what I want to do while chasing a production memory leak.

The Junie Thing Is Overhyped

Junie is their "autonomous coding agent" but it's basically GPT with extra steps. I tried it for implementing a GraphQL resolver and it generated working code, but nothing I couldn't do faster with regular AI chat. The $39/month for AI Ultimate with Junie access isn't worth it unless you love burning money on features you'll use twice.

Code Completion That Finally Gets Your Project

Forget the marketing fluff - here's what actually happens: you start typing a method and JetBrains AI suggests the whole implementation based on your project's patterns, not random Stack Overflow garbage. When I type validateUser in our Spring Boot app, it knows we're using Bean Validation with custom validators and suggests the actual annotations we use.

Real example from last Tuesday:
Started typing public ResponseEntity<UserDto> createUser and the bastard generated the entire method with proper exception handling, validation, and DTOs that actually matched our existing code style. Saved me 20 minutes of typing. No generic shit, no deprecated patterns - it knew our MapStruct setup and ResponseEntity patterns.

Languages where it doesn't completely suck:
Java/Kotlin with Spring - Actually knows your annotations and doesn't suggest WebSecurityConfigurerAdapter like some tools (cough Copilot). When I type @Transactional it understands our custom transaction manager setup.

TypeScript with React - Understands component patterns and hooks without trying to mix class components with functional ones. Though it did once suggest useState in a class component, which... how?

Python with FastAPI - Generates decent Pydantic models that actually validate things. Better than my usual "eh, it's probably a string" approach to API schemas.

Languages where it makes me question my career choices:
Go keeps suggesting error handling patterns from 2018. I asked it to implement context cancellation and it generated code that would make Rob Pike cry. Seriously, who trained this thing on Go 1.9 examples?

Rust is passable for basic syntax but completely misses lifetime and ownership subtleties. Asked it to help with a concurrent HashMap and it suggested Arc<Mutex> everywhere like it was fucking 2018. My borrow checker had a nervous breakdown and so did I.

Anything newer than what was in its training data - good luck getting help with the latest Node.js features or fresh framework releases. It's like asking your grandpa about TikTok trends.

AI Chat: When Stack Overflow Isn't Enough

The AI Chat is where JetBrains AI actually shines. I paste error messages like this clusterfuck:

Caused by: org.springframework.beans.factory.UnsatisfiedDependencyException: 
Error creating bean with name 'userController': Unsatisfied dependency 
expressed through field 'userService'

And it tells me exactly what's wrong with my Spring configuration, not generic advice about "check your dependencies." It knows I'm using @Service annotations and suggests the specific component scanning fix.

Actually useful features:

• Screenshot analysis - Takes screenshots of error dialogs and explains them
• Multi-file context - Attach multiple files so it understands your architecture
• Iterative debugging - Remembers the conversation so you can drill down on issues
• Project-aware suggestions - Knows your framework versions and dependencies

Where it completely shits the bed:
Context window fills up during long debugging sessions and suddenly it's like talking to someone with dementia. Had a 2-hour session debugging our Kafka consumer timeout issue and halfway through it forgot we were even using Kafka. "What's a Kafka?" it basically asked. Fucking brilliant.

Sometimes hallucinates Spring Boot properties that don't fucking exist. Spent an hour yesterday trying to configure server.ssl.key-store-provider because it insisted that was a real property. Spoiler: it's not. The actual property name is different and buried in some sub-configuration class.

GPT-5 support is marketing bullshit - I can't tell the difference from GPT-4 except it costs more credits. My debugging sessions aren't magically faster, my code isn't magically better. It's the same responses with a fancier model name.

Code Generation: The Good, The Bad, The Expensive

Right-click, select "Generate Unit Tests" and watch your credits burn. But the tests it generates are actually useful - proper JUnit 5 with Mockito mocks that test edge cases, not just happy path bullshit.

What works:

• Unit test generation with realistic test data and edge cases
• JavaDoc/JSDoc generation that's actually readable
• REST endpoint boilerplate following your project's patterns
• Converting callbacks to async/await without breaking shit

What's expensive as hell:

• Refactoring large classes (15+ credits for a service with 10 methods)
• Generating comprehensive test suites (8-12 credits per test class)
• Complex algorithm implementations (10+ credits for anything non-trivial)

Git Integration That Doesn't Suck

The commit message generation actually reads your staged changes and writes decent commit messages. Instead of "fix bug" it generates stuff like:

feat: add JWT token validation with proper exception handling

- Implement TokenService with RS256 signature verification
- Add custom InvalidTokenException for expired tokens  
- Update security config to use new validation logic

Follows Conventional Commits and doesn't cost many credits (usually 1 credit for typical commits).

Local Models: Privacy Without Performance

You can run local models through OpenAI-compatible APIs like LM Studio or Ollama. Set it up once, use it forever without credit anxiety.

Reality check:

• Local Code Llama is slow as hell on my MacBook Pro
• Quality is noticeably worse than GPT-4 or Claude
• Good for basic completion, terrible for complex debugging
• Zero network latency is nice but not worth the performance hit

The Junie Agent: $39/Month for What Exactly?

Junie is JetBrains' "autonomous coding agent" that costs extra on top of AI Ultimate. I tested it for a month because FOMO is real.

What I tested:

1. "Implement user authentication with JWT" - Generated working Spring Security config but nothing I couldn't do with regular AI chat
2. "Fix all TODO comments in this service" - Actually worked well, updated 8 methods across 3 files
3. "Add logging to all service methods" - Added proper SLF4J logging with context

The verdict: Junie is GPT-4 with a fancy progress bar. Useful for large refactoring tasks but not worth $39/month unless you're constantly doing massive code changes. Regular AI chat + copy/paste gets you 90% there for way less money.

Setup: Easy Part vs Corporate Hell

If you're on IntelliJ 2025.1+, it's already installed - just activate it. Older versions need the plugin from the marketplace, which takes 30 seconds unless your corporate proxy is fucked (spoiler: it probably is).

What actually happens during setup:

1. Go to jetbrains.com/ai-ides/buy and pick a plan (don't start with free, it's useless)
2. Settings → Tools → AI Assistant, paste your license key
3. Pick your AI models - GPT-4 is fastest, Claude 3.5 is best for code explanations
4. Turn off the features you won't use to save credits

Enterprise setup horror stories:

• Proxy settings will break everything - you'll need IT help
• BYOK requires 47 security approvals
• Local models need firewall exceptions your security team will hate

How I Learned to Stop Burning Credits Like an Idiot

The credit system is designed to extract maximum cash from desperate developers at 3am. Once burned 30 credits in one session trying to debug why our Docker build was failing. Turns out I had a fucking typo in the Dockerfile - missing an 's' in WORKDIR. $30 to find one missing letter. That's when I got serious about credit management.

How to not go completely broke:
Start new chats for unrelated shit - don't let context build up and eat credits. I made this mistake debugging authentication issues and somehow ended up paying for the AI to remember our entire conversation about database migrations.

Use the cheaper models for simple completion. GPT-4 for "why is my build failing" is overkill when GPT-3.5 can spot a syntax error just fine.

Batch related questions together. Instead of asking "fix this method" then starting a new chat to ask "now test this method", do it all in one session. Learned this the expensive way.

Local code completion doesn't use credits but it's slower than dial-up in a thunderstorm. Good for when you're rationing credits at the end of the month.

Monitor your credit usage through the AI Assistant widget in the IDE toolbar. The progress bar provides real-time feedback on remaining quota, helping you adjust usage patterns throughout the month.

Prompts That Don't Suck

After 8 months of burning credits on bad prompts, here's what actually works:

Don't say: "Write a function" (AI will generate generic garbage)
Say: "Generate a Spring Boot REST endpoint for user registration with BCrypt password hashing and proper validation errors"

Don't say: "Fix this code" (what fucking code? fix what?)
Say: "This JWT validation is throwing NullPointerException on line 47 when the token is malformed - here's the stack trace: [paste actual error]"

The AI needs context about your actual codebase, not vague requests. Tell it you're using Spring Security 6, not 5. Mention your validation framework. Show it your existing patterns.

Credit Anxiety Management (AKA Not Going Broke)

The real trick is being strategic about when to burn credits:

• Use local models for simple completion - save credits for complex debugging
• Start fresh chats - don't let context build up and eat credits
• Batch related questions - "While you're at it, also explain this other error"
• Copy paste intelligently - don't ask it to read entire files, just the relevant methods

Track my credit usage in a spreadsheet now because I'm neurotic about this shit. May 2025: 34 credits debugging authentication that turned out to be a misconfigured CORS policy. June: 51 credits on that Docker networking nightmare. July: 23 credits implementing WebSocket connections that kept disconnecting randomly.

Team Setup Reality

Getting your team on JetBrains AI without bankrupting the company:

Start with pilot program - Pick 2-3 senior devs who won't go crazy with credits
Set credit limits - $50/month per developer maximum until you know usage patterns
Share war stories - What worked, what was expensive, what to avoid
Have fallback plans - When someone hits their limit at 2am during an outage

The enterprise features are mostly security theater. BYOK takes 6 weeks to approve and doesn't work half the time anyway.