You’ve been there. You ask your AI coding assistant to add a simple feature. It starts confidently. Twenty files get touched. And suddenly your app is broken in ways that make no sense.

The AI didn’t suddenly get dumb. You accidentally triggered a known failure mode.

You’re Asking It to Multitask

Here’s what clicked for me after months of frustration: large language models are terrible at doing multiple cognitive tasks at the same time.

When you type “add user authentication to my app,” you’re actually asking for:

  • Analysis of your current codebase structure
  • Architectural decisions about where auth should live
  • Security considerations
  • Implementation of multiple components
  • Integration with existing code

That’s five different types of thinking. When an LLM tries to do all of them at once, quality tanks.

It’s like asking someone to plan a road trip while also driving, navigating, and having a deep conversation. Something’s gonna suffer.

Split Thinking and Doing

The fix is almost too obvious: separate the thinking from the doing.

But watch how most developers use Cursor or Claude:

“Build me a dashboard with charts showing user activity, filterable by date range, with export to CSV.”

That’s a mini-project crammed into one prompt. The AI has to figure out what charts, what data structure, what components, what libraries, AND write all the code. At the same time.

Try this instead:

First prompt: “I need a dashboard with user activity charts. Before writing any code, analyze my current codebase and tell me: What’s the best approach? Where should the components live? What existing patterns should I follow?”

Let it think. Read the output. Ask follow-up questions.

Then: “Great, now implement just the chart component. Nothing else.”

One piece at a time.

Use Multiple Models as a Review Board

This felt weird at first but works well: use different AI models to cross-check each other.

Let’s say Cursor gives you an implementation plan. Before you execute it, paste that plan into Claude (or ChatGPT, or Gemini) and ask: “What’s wrong with this plan? What am I missing?”

Different models have different blind spots. Claude might catch security issues that Cursor missed. Gemini might point out a simpler approach. It’s like having multiple engineers review your architecture before you build.

Takes five extra minutes. Saves hours of debugging later.

Ask for an Audit First

Before any significant change, ask for an audit instead of an implementation.

Bad: “Refactor my authentication system”

Better: “Audit my authentication system. Don’t change anything yet. Tell me: What’s working well? What are the security risks? What would you change and why? What order should we do this in?”

You get a map before you start walking. And you spot bad ideas before they become bad code.

That “don’t change anything yet” part matters. It forces the model into analysis mode.

Small Chunks, Constant Testing

Once you have a plan you trust, execute it in tiny pieces.

I mean tiny. “Add the login form JSX” is one task. “Add form validation” is another. “Wire up the submit handler” is a third.

After each chunk:

  1. Read every line the AI wrote
  2. Make sure it didn’t “helpfully” change something unrelated
  3. Test that specific piece
  4. Then move on

Yes, it’s slower than letting the AI rip through everything. But you catch problems when they’re small. Debugging a 5-line change is easy. Debugging 50 files is a nightmare.

When It Goes Sideways

It will happen. The model will start heading somewhere weird.

Don’t try to course-correct mid-stream. It doesn’t work. The AI tries to merge its wrong approach with your correction and you get frankencode.

Better: Stop completely. Start a new chat. Restate the goal simply. Fresh context, fresh start.

Also useful: Ask the AI why it did what it did. “Explain your reasoning for that approach.” Sometimes it reveals a misunderstanding you can fix. Sometimes its reasoning is actually sound and you should reconsider.

Write Tests First

Instead of: “Write a function that validates email addresses”

Try: “Write tests for an email validation function first. Cover normal emails, edge cases, and invalid inputs. Then implement the function. Then run the tests and fix any failures.”

Now the AI has a target. It can’t just write plausible-looking code and call it done. It has to make something that actually passes.

The tests become a contract. You can verify the contract makes sense before any implementation happens.

Keep Your .cursorrules Short

Like, really short.

Don't modify code outside the specific request.
Never use placeholder comments like "rest of code here."
Explain your approach before implementing.
Ask clarifying questions if the request is ambiguous.

That’s pretty much it. The model already knows how to code. It just needs boundaries.

Some people add project context:

This is a Next.js 14 app with TypeScript.
State management: Zustand
Testing: Vitest
Run tests with: npm test

Helpful, but keep it factual. Long philosophical rules usually get ignored.

The Short Version

  1. Ask for analysis first, not code
  2. Cross-check plans with another model
  3. Execute in small chunks, test each one
  4. Stop and restart when things go sideways
  5. Write tests first for anything important

Why This Works

AI coding tools are limited in ways that aren’t obvious at first. They can’t maintain context across complex multi-step reasoning while also writing code. They miss things. They confidently write broken code.

Once you accept that, the workflow makes sense. You’re not fighting the tool anymore. You’re working with how it actually behaves.

These tools are good. They just need structure.