AI coding tools like Cursor are incredibly powerful — but how you prompt them determines whether you get a clean 5-line fix or a 500-line rewrite.

This guide explains how to reduce tokens, reduce cost, and improve output quality by tightening your prompts and controlling context.

CORE RULE

Smaller context + tighter instructions = cheaper and better results

Large prompts = more analysis = more tokens = slower + noisier responses.

INPUT TOKEN SAVING (What YOU Send)

These techniques reduce how much context the AI has to read.

1️⃣ Select Only the Relevant Code

Send only what needs to change.

❌ Bad: Improve validation in this file

✅ Good: Improve validation in this function

2️⃣ Avoid Repeating Architecture Every Time

Don’t restate your stack repeatedly.

Put these in Cursor Rules instead.

❌ We are using Next.js app router with hooks and services…

✅ Follow project rules. Refactor this hook.

3️⃣ Don’t Paste Logs Unless Necessary

Logs are extremely token-heavy.

❌ Full build log
✅ Only the error message + related code

4️⃣ Start New Chats for New Tasks

Old chat history is reprocessed every time.

New feature?
➡️ Start a new chat

5️⃣ Don’t Open Unrelated Files

Cursor reads open tabs as context.

Close things like:

Config files
Large JSON files
Constants files
Unrelated components

Less open context = cheaper and more focused responses.

6️⃣ Use References Instead of Explanations

Don’t describe architecture if the AI can follow patterns.

❌ We have a folder where services call APIs and hooks manage state…

✅ Follow existing services folder pattern

7️⃣ Scope the Change Clearly

Broad prompts increase analysis tokens.

❌ Improve this component

✅ Add loading and error state to this hook only

OUTPUT TOKEN SAVING (What AI Sends Back)

Now we control how much the AI writes.

8️⃣ Ask for Code Only

Biggest output saver.

Use phrases like: Code only. No explanation.

9️⃣ Ask for Minimal Changes

Prevents full rewrites.
Modify existing code, don’t rewrite
Return only the updated function

Limit Refactor Scope

❌ Rewrite this page using best practices

✅ Refactor API logic into a separate service

1️⃣1️⃣ Don’t Ask for Teaching Mode Unless Needed

Explanations can be 2–3× longer than code.

Only when learning: Explain why this bug happens

Otherwise: Fix only

1️⃣2️⃣ Control Verbosity Explicitly

You can directly instruct response size:

Short answer
Minimal code
No comments

WORKFLOW HABITS THAT SAVE A LOT

These habits reduce repeated token waste over time.

1️⃣3️⃣ Iterate Instead of Regenerate

If the result is 80% correct:

❌ Regenerate
✅ Keep everything same, just fix the API error handling

1️⃣4️⃣ Use Edits, Not Generations

Editing existing code uses fewer tokens than generating new files.

Prefer: Modify this component

Over: Create a new improved version

1️⃣5️⃣ Avoid “Best Practices” Prompts

This triggers long explanations and rewrites.

❌ Improve this using best practices

✅ Extract API call into service function

1️⃣6️⃣ Don’t Ask for Documentation by Default

Docs produce large outputs.

❌ Document this code

✅ Add short JSDoc comment

Final Takeaway

Good prompting isn’t about being polite — it’s about being precise and restrictive.

If you remember only one pattern, use this:

Fix with minimal change. Code only.

That one line alone can cut token usage dramatically while keeping diffs clean and review-friendly.

Cursor Prompting Guide: Get Better Code with Fewer Tokens