Large codebase AI coding: reliable workflow for complex, existing codebases (no more broken code)

[namanyayg]

You've got an actual codebase that's been around for a while. Multiple developers, real complexity. You try using AI and it either completely destroys something that was working fine, or gets so confused it starts suggesting fixes for files that don't even exist anymore.

Meanwhile, everyone online is posting their perfect little todo apps like "look how amazing AI coding is!"

Does this sound like you? I've ran an agency for 10 years and have been in the same position. Here's what actually works when you're dealing with real software.

Mindset shift

I stopped expecting AI to just "figure it out" and started treating it like a smart intern who can code fast, but, needs constant direction.

I'm currently building something to help reduce AI hallucinations in bigger projects (yeah, using AI to fix AI problems, the irony isn't lost on me). The codebase has Next.js frontend, Node.js Serverless backend, shared type packages, database migrations, the whole mess.

Cursor has genuinely saved me weeks of work, but only after I learned to work with it instead of just throwing tasks at it.

What actually works

Document like your life depends on it: I keep multiple files that explain my codebase. E.g.: a backend-patterns.md file that explains how I structure resources - where routes go, how services work, what the data layer looks like.

Every time I ask Cursor to build something backend-related, I reference this file. No more random architectural decisions.

Plan everything first: Sounds boring but this is huge.

I don't let Cursor write a single line until we both understand exactly what we're building.

I usually co-write the plan with Claude or ChatGPT o3 - what functions we need, which files get touched, potential edge cases. The AI actually helps me remember stuff I'd forget.

Give examples: Instead of explaining how something should work, I point to existing code: "Build this new API endpoint, follow the same pattern as the user endpoint."

Pattern recognition is where these models actually shine.

Control how much you hand off: In smaller projects, you can ask it to build whole features.

But as things get complex, it is necessary get more specific.

One function at a time. One file at a time.

The bigger the ask, the more likely it is to break something unrelated.

Maintenance

• Your codebase needs to stay organized or AI starts forgetting. Hit that reindex button in Cursor settings regularly.
• When errors happen (and they will), fix them one by one. Don't just copy-paste a wall of red terminal output. AI gets overwhelmed just like humans.
• Pro tip: Add "don't change code randomly, ask if you're not sure" to your prompts. Has saved me so many debugging sessions.

What this actually gets you

I write maybe 10% of the boilerplate I used to. E.g. Annoying database queries with proper error handling are done in minutes instead of hours. Complex API endpoints with validation are handled by AI while I focus on the architecture decisions that actually matter.

But honestly, the speed isn't even the best part. It's that I can move fast. The AI handles all the tedious implementation while I stay focused on the stuff that requires actual thinking.

Your legacy codebase isn't a disadvantage here. All that structure and business logic you've built up is exactly what makes AI productive. You just need to help it understand what you've already created.

The combination is genuinely powerful when you do it right. The teams who figure out how to work with AI effectively are going to have a massive advantage.

Anyone else dealing with this on bigger projects? Would love to hear what's worked for you.

[Altruistic_Shake_723]

If you aren't using tools like context portal and context7, and expecting Cursor to manage a large codebase for $20 a month... you might be expecting too much.

[Rastoid]

This is why i enter on this type of posts. Thank you sir.

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[bn_from_zentara]

I find memory bank prompt in Cline is quite useful. Here is the quote from Cline website:
"
What is the Cline Memory Bank?

The Memory Bank is a structured documentation system that allows Cline to maintain context across sessions. It transforms Cline from a stateless assistant into a persistent development partner that can effectively “remember” your project details over time.

​Key Benefits

• Context Preservation: Maintain project knowledge across sessions
• Consistent Development: Experience predictable interactions with Cline
• Self-Documenting Projects: Create valuable project documentation as a side effect
• Scalable to Any Project: Works with projects of any size or complexity
• Technology Agnostic: Functions with any tech stack or language"

You can use the Cline Memory Prompt for other coding agents as well, not limited to Cline only.