A practical wiki guide on using ChatGPT and Cursor efficiently for thinking, coding, debugging, and learning workflows.
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How to Efficiently Use ChatGPT and Cursor

Overview

ChatGPT and Cursor are extremely effective tools for AB testing, experimentation, and CRO development when used with the right mindset. This wiki focuses on how frontend engineers, CRO specialists, and experimentation teams can use these tools to build faster, debug safer, and reason better while working inside tools like AB Tasty, Optimizely, VWO, or custom experiment frameworks.

Core principles for AB testing:

  • ChatGPT helps with thinking → hypotheses, logic, edge cases, and reasoning
  • Cursor helps with execution → writing, refactoring, and understanding experiment code in real codebases

Used together, they reduce iteration time while keeping experiments safe and maintainable.

When to Use ChatGPT vs Cursor

ChatGPT is best for:

  • Understanding concepts and mental models
  • Designing approaches or architectures
  • Debugging logic and edge cases
  • Writing or reviewing algorithms
  • Learning unfamiliar APIs or patterns

Cursor is best for:

  • Working inside an existing codebase
  • Inline code generation and refactoring
  • Navigating large repositories
  • Understanding unfamiliar code quickly
  • Writing tests and boilerplate

Rule of thumb:

Ask ChatGPT what to do. Use Cursor to do it faster.

Writing High-Quality Prompts for AB Testing

High-quality prompts are critical in experimentation, where code often runs in production, on third-party sites, and under strict constraints.

Be Explicit About Experiment Context

Avoid generic prompts:

❌ Fix this JS

✅ This code runs inside an AB Tasty experiment on a production e-commerce site. Refactor it to avoid duplicate DOM observers and ensure it is safe for SPA navigation.

Always Mention Constraints

In AB testing, always include:

  • Experiment platform (AB Tasty / Optimizely / VWO / Convert)
  • Page type (PLP / PDP / Checkout)
  • SPA vs MPA behavior
  • Browser support (especially iOS Safari)
  • Whether performance or flicker is a concern

Example:

AB Tasty experiment
Runs on PDP and PLP
SPA navigation enabled
No external libraries
Must avoid layout shift

Iterative Prompting (Very Important)

Treat ChatGPT like a senior engineer you collaborate with—not a magic button.

Recommended flow:

  1. Ask for a solution
  2. Review the response
  3. Add constraints
  4. Ask for optimizations
  5. Ask about edge cases
  6. Request cleanup or refactor

Example:

  • “Write a MutationObserver for this DOM change”
  • “Optimize it to avoid unnecessary re-renders”
  • “Make it safe for SPA navigation”
  • “Add cleanup logic”

Debugging AB Test Issues with ChatGPT

AB test bugs are often timing- and DOM-related, making them hard to debug without structured thinking.

Ask About Root Causes

❌ This experiment breaks sometimes

✅ Why might this MutationObserver fire multiple times during SPA navigation, and how can I guard against re-initialization in an AB test?

Provide Experiment Signals

To get useful answers, include:

  • URL patterns and page transitions
  • When the experiment initializes
  • MutationObserver or polling logic
  • Console logs from multiple navigations
  • Control vs variant behavior

ChatGPT is especially good at identifying race conditions, duplicate observers, and missing cleanup logic.

Cursor Best Practices for Experiment Code

Understanding Legacy Experiment Code

AB testing often involves reading old or inherited experiment code. Cursor excels here.

Highlight code and ask:

  • What is this experiment trying to change on the page?
  • Is this observer safe if the user navigates back and forth?
  • Could this cause duplicate DOM injections?

Safe Refactoring of Variants

Instead of:

Clean this code

Use:

Refactor this AB test code to be readable and modular without changing behavior or selector logic.

Generating Guarded Utilities

Cursor is useful for generating:

  • defineOptiReady-style helpers
  • Safe waitUntil utilities
  • Observer cleanup logic
  • Reusable experiment init patterns

Always review for over-triggering risks.

When to Double-Check AI Output

Never blindly trust output when dealing with:

  • Security-sensitive code
  • Regex
  • Time zones and dates
  • Floating-point calculations
  • Performance-critical logic

AI output should be treated as a first draft, not the final answer.

Using ChatGPT for Faster Learning

As a Tutor

Helpful prompts:

  • Explain this like I’m new
  • Give me a mental model
  • Show a bad vs good example

Practice-Driven Learning

Example:

Give me 3 practice problems for MutationObserver with increasing difficulty.

This reinforces understanding far better than passive reading.

Common Mistakes to Avoid

  • Asking vague or underspecified questions
  • Copy-pasting without understanding
  • Using AI as a replacement for thinking
  • Ignoring edge cases
  • Not validating against real data
  1. Define hypothesis and expected user impact
  2. Ask ChatGPT for implementation approach and edge cases
  3. Implement the variant using Cursor
  4. Add guards for re-initialization and SPA navigation
  5. Manually test across navigations and devices
  6. Ask ChatGPT to review for performance, safety, and cleanup

This workflow significantly reduces flaky experiments and QA issues.

Conclusion

In AB testing, mistakes are costly—experiments run in production, at scale, on real users.

ChatGPT and Cursor help experimentation teams:

  • Think through edge cases before shipping
  • Write safer, more maintainable variant code
  • Debug faster when things go wrong
  • Learn patterns that reduce future risk

They don’t replace CRO engineers—they make good experimentation engineers much more effective.

Last updated: January 2026

Tags: tools productivity ai