The easiest way to save your tokens when you are coding using AI tools even if you have no coding experience is to Just know your codebase well.

Even before you start, write down your idea over a whiteboard and in a Word document, thoroughly explore it.

Have some idea about which library might be used and if you have no clue about that, ask AI.

Once you have a fair bit of idea, Vibe code and have the clear instructions for AI to create a manageable but partitioned directory which is separated as per different functions

And now when you face some error or some obstacle ask AI, Which files are related to this?

Copy those files.

And give it to some external AI like claude or deepseek.

Tell it that you want improvements in this piece of code.

“Tell me what it is missing. Be very thorough about the bugs or problematic pieces of the code. Do not fix the code but just give me analysis and just focus on that code I gave you. Do not think about anything other than this.”

With a prompt like this, it will do extensive analysis and give you a chunk of text that you should give to cursor or whatever tool you are using to code.

This will save you tons of tokens because you used some external AI.

Cursor didn’t have to spend tokens to do it and you will get much better results.

You will also stay in loop by knowing that

Hey this thing is done by this file.

That thing is done by that file.

It is better for you to do it. This way you will save tokens and have more knowledge about your own project.

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Claude’s reaction:

Your strategy breaks down into smart phases:

1. Planning Phase (Pre-coding)

• Whiteboard/document your idea thoroughly
• Research relevant libraries upfront
• Ask AI for architecture recommendations before diving in

2. Structure Phase (Initial coding)

• Use AI to create a well-organized, modular directory structure
• Partition by function/feature from the start
• This modularity pays dividends later

3. Problem-Solving Phase (The token-saver)

• When you hit errors, identify which specific files are involved
• Extract only those files
• Use a separate AI instance (Claude, DeepSeek, etc.) for analysis
• Get thorough diagnostics without burning tokens in your coding tool
• Feed that analysis back to Cursor/your main tool

Why this works:

• Token efficiency: Analysis happens outside your primary tool’s token budget
• Better understanding: You’re forced to understand file relationships
• Focused feedback: The external AI analyzes specific code, not your entire project
• Quality: Dedicated analysis often beats rushed fixes

One addition I’d suggest: Keep a simple markdown file documenting which files handle what functionality. As you learn “this file does X, that file does Y,” write it down. Future-you will thank present-you when debugging at 2 AM.