Rocket Blogs
Engineering
The work is only as good as the thinking before it.
You already know what you're trying to figure out. Type it. Rocket handles everything after that.
Rocket Blogs
Engineering
You already know what you're trying to figure out. Type it. Rocket handles everything after that.
Table of contents
Every developer has lived this nightmare: It's your first day on a new project. You open the repository. There's a utils folder with 47 files. Half the components use kebab-case, the other half use camelCase. There's a NewButton.tsx and a Button.tsx and a ButtonComponent.tsx, and you have no idea which one you're supposed to use. Someone started implementing authentication but gave up halfway through. The README was last updated in 2021.
Welcome to reality.
This is the problem every "AI code generation" tool conveniently ignores. They're great at generating code from scratch—give them a blank canvas and a prompt, and they'll build you a beautiful, consistent, perfectly-architected application. But the moment you ask them to continue someone else's work? Silence.
At Rocket, we decided to solve the problem everyone else was avoiding.
Here's the thing about AI code generation in 2026: every tool can generate code from scratch. Cursor, GitHub Copilot, Lovable, Replit—they're all excellent at building new applications. Give them a clean slate and clear requirements, and they'll produce high-quality code that follows best practices.
But that's not how real development works.
Real development is inheriting a six-month-old Next.js project where:
fetch, others use axios, and a few brave souls tried ky"strict": false because "we'll fix it later"services/ folder, a utils/ folder, and a helpers/ folder, and nobody remembers the differenceNow try asking an AI to add authentication to that codebase. Should it use the existing pattern or start fresh? Should it follow the inconsistent naming conventions or fix them? Should it use fetch or axios? Should it add to services/, utils/, or helpers/?
This is the question that defines whether AI code generation is a toy or a tool.
When we started building Rocket's GitHub integration, we thought the hard part would be the Git operations—cloning repos, managing branches, handling merge conflicts. Those turned out to be the easy parts.
The hard part was teaching AI to do what every senior engineer does instinctively on their first day: read the room.
Generating new code is pattern matching. Understanding existing code is archeology.
When you connect a GitHub repository to Rocket, we don't just clone the files—we analyze the entire codebase to answer questions like:
auth/ folder a fully-implemented feature or an abandoned experiment?).component.tsx and others don't?)The difference between a junior developer and a senior developer is that the senior developer spends their first week reading code before writing any. We had to teach AI to do the same thing.
Here's where it gets philosophically interesting.
Let's say you inherit a codebase where every component file is 800 lines long—clearly violating best practices. As an AI system, should you:
A) Follow the existing pattern (write new 800-line component files to maintain consistency)
B) Fix the existing pattern (break components into smaller pieces, refactor the entire codebase)
C) Do something in between (write smaller components but don't touch the existing ones)
The answer depends on context humans understand but machines don't:
We built a system that:
This is my favorite problem because it's so uniquely human.
You clone a repository and find:
auth/ folder with login.tsx, register.tsx, and forgot-password.tsxuseAuth hook that's implemented but never imported anywhere/api/auth/login and /api/auth/register but not /api/auth/logoutAUTH0_DOMAIN but the actual Auth0 integration is commented outIs authentication implemented or not?
A human developer would:
We had to teach AI to do the same detective work. Now when Rocket analyzes a codebase, it doesn't just look at what files exist—it looks at:
If a feature is half-finished, Rocket tells you: "I found an auth folder, but it doesn't look complete. Want me to finish it or start fresh?"
Let me show you what this looks like in practice.
When you connect a GitHub repository to Rocket, the first thing we do is run it through our codebase analyzer. This isn't a simple file tree inspection—it's a full architectural audit.
For a typical Next.js project, we identify:
For monorepos, we go deeper:
Once we understand the codebase, Rocket doesn't just generate code—it continues the story.
If you ask Rocket to "add authentication," it doesn't give you a generic auth implementation. It:
Here's where it gets really interesting. Every change Rocket makes is:
rocket-update)This isn't "AI overwrites your code." This is "AI joins your team."
You can:
Building this feature taught us that edge cases are the real product.
The scenario: A codebase where:
/api/user-profile, /api/order-historyUserProfile.tsx, OrderHistory.tsxgetUserProfile(), getOrderHistory()What most AI tools do: Pick one convention arbitrarily (usually whatever's in their training data).
What Rocket does: Recognize that each domain has its own convention, respect all three, and apply the correct one based on what type of file is being generated.
The scenario: The codebase uses a custom fetcher wrapper around fetch that adds authentication headers, retries, and error handling. But it's not documented anywhere.
What most AI tools do: Generate code using vanilla fetch (because they don't know the wrapper exists).
What Rocket does:
fetcher functionHere's the uncomfortable truth: most "AI code generation" tools are built for demos, not for work.
They're optimized for the TechCrunch headline: "Watch AI build a full-stack app in 60 seconds!" They generate beautiful greenfield projects that make great screenshots.
But real development isn't greenfield projects. Real development is:
The ability to understand and continue existing code is the difference between a coding assistant and a coding teammate.
At Rocket, we didn't just build a tool that generates perfect code from scratch. We built a tool that can jump into your messy, real-world codebase and start contributing like a senior engineer.
When AI can actually understand existing codebases, suddenly a lot of previously-impossible workflows become possible:
Instead of spending your first week reading code, you can ask Rocket:
And get architectural answers, not just file locations.
"This UserDashboard.tsx file is 1,200 lines. Break it into smaller components following the existing patterns in this codebase."
Rocket doesn't just split the file—it:
"Generate a README for this codebase that explains the architecture, conventions, and how to add new features."
Because Rocket actually understands your code, it can write documentation that's specific to your project, not generic boilerplate.
"Migrate this component from Pages Router to App Router while keeping the same functionality."
Because Rocket understands what the component does (not just what it says), it can translate intent across different patterns.
The most exciting one: AI becomes a team member that understands your codebase.
Instead of every developer working with their own isolated AI assistant that has no memory of the project, your whole team shares a Rocket workspace that:
Building this feature taught us three things:
The same code change is good or bad depending on context. Adding type safety is great in a new project, but if you're working in a codebase with "strict": false, forcing strict types will break everything. AI needs to understand context, not just syntax.
In existing codebases, maintaining consistency with "okay" patterns is usually better than introducing "perfect" patterns that break consistency. A codebase where everything follows the same mediocre pattern is easier to work with than a codebase with a mix of perfect and mediocre patterns.
Developers won't trust AI that silently changes code. Every change needs to be visible, reviewable, and reversible. That's why we built the PR workflow—not because Git operations are hard, but because trust requires visibility.
Every other AI coding tool is racing to build better code generation. Faster models. Longer context windows. Better prompting.
We're racing in a different direction: teaching AI to understand the code that already exists.
Because here's the reality: the world has billions of lines of existing code, and only a tiny fraction of new code written every day. If AI can't understand and continue existing code, it's limited to greenfield projects—which is maybe 5% of real development work.
At Rocket, we're building for the other 95%.
If you've ever inherited a codebase and wished you had a senior engineer to help you understand it, Rocket's GitHub integration is built for you. It's the first AI tool that actually tries to understand your code instead of just generating new code next to it. Because the future of AI-assisted development isn't generating code from scratch. It's understanding the code we've already written—and helping us make it better.
Try Rocket's GitHub integration: rocket.new
Read the technical docs: docs.rocket.new/build/create/from-github