How to Turbocharge Your Coding with ChatGPT and Codex

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If you want to code at a completely different speed, AI should now be part of your development cycle.

I have written about this in several other articles, and I have also made my position clear: developers and programmers are not going away. I still firmly believe that.

But — and this is a very big but — the role of the developer is changing.

You will likely spend less time manually writing every line of code and far more time planning, directing, testing, refining, and communicating with AI tools. In many cases, the real skill is no longer just writing code. It is knowing how to guide AI effectively so that it produces useful, consistent, and maintainable results.

So how do you use AI in the most efficient and productive way?

The Difficult Approach to AI Coding

You might be tempted to jump straight into Codex, or a similar AI coding app, and begin building immediately.

That can work.

But in practice, many people quickly lose direction. The project starts drifting, prompts become inconsistent, and before long the AI is solving one small problem at a time without a strong understanding of the larger objective. Unless you are already very skilled at writing precise prompts and maintaining continuity across a long development cycle, this approach can become frustrating.

The Better Approach

A much better method is to begin with a planning tool such as ChatGPT, and then use Codex as the implementation engine.

For this example, I will use ChatGPT + Codex as the workflow.

Think of it like this:

• ChatGPT helps you think, plan, structure, critique, and prepare
• Codex helps you execute, build, test, and modify the codebase

That combination is incredibly powerful.

Step 1: Use ChatGPT to Define the Project Properly

Before you write a single line of code, ask ChatGPT to help you create a structured outline of your project.

This should include:

• the purpose of the project
• the core features
• the development stack, such as Node.js, Python, PHP, or another language
• the database choice, such as MySQL, PostgreSQL, or MongoDB
• the proposed folder structure
• the data structure and relationships
• the testing approach
• the likely weak points or risks
• areas where mission creep could become a problem

This stage matters more than many people realise.

Everything leads back to structure. If your data model and application layout are poorly thought through at the beginning, expansion later becomes harder, messier, and more expensive in time.

Ask ChatGPT to challenge your idea as well. Tell it to point out weaknesses, possible scalability issues, logic risks, and any feature creep that might derail the build. If needed, repeat this stage several times until you are happy with the outline, goals, and realistic development path.

Also make it clear from the beginning that you want testing procedures included as the project evolves, so the AI-generated code can be validated at each stage.

Step 2: Set Up the Project Properly

Once you have a solid plan, create a folder for the project and initialise it with Git.

Even if you are new to Git, it is worth using. You do not strictly need it to continue, but it is highly recommended. Git allows you to save the state of your project at key stages. If something goes badly wrong, you can return to a previous working version with a commit.

That safety net becomes even more important when AI is generating and changing code quickly.

If you have not already done so, install the Codex app and make sure it is ready to access your local project folder.

Step 3: Create an AGENT.md File

This is a very important step.

Ask ChatGPT to create an AGENT.md file for your project.

An AGENT.md file is essentially an instruction document for the coding agent. It tells Codex how it should behave while working on the project. Depending on how you structure it, it can include:

• the project purpose
• the stack being used
• coding standards
• file organisation rules
• testing requirements
• naming conventions
• things the agent should avoid
• the order of priorities
• project goals and constraints

In simple terms, it acts like a briefing document that keeps the AI aligned with your intentions.

Without something like this, an AI coding tool may still generate useful code, but it has less context to stay consistent over time.

Step 4: Add the Existing Project to Codex

Open the Codex app and choose the option to add an existing project.

Select your project folder and continue.

At this point, Codex has access to the project files and the supporting context you have prepared. That means it is no longer working blindly. It has structure, instructions, and a defined starting point.

Step 5: Use ChatGPT to Create the Prompt Sequence

Now ask ChatGPT to generate the set of prompts you should feed into Codex to begin building the project.

This is where the workflow becomes very effective.

Rather than improvising each next step yourself, ask ChatGPT to:

• break the build into stages
• write the prompts for each stage
• suggest the best order to run them
• keep the prompts focused and clear
• include testing expectations where relevant

This reduces drift and helps keep development structured.

In other words, ChatGPT becomes your planning and prompt-engineering assistant, while Codex becomes your coding and implementation tool.

Step 6: Run One Prompt at a Time

Take the first prompt and give it to Codex.

Let Codex complete the task fully before moving on.

When it finishes, it should usually provide:

• the files it changed or created
• a summary of what it implemented
• instructions on how to run the project, if applicable
• test results showing what was checked

This staged process is important. Do not rush ahead by piling in five changes at once. The more controlled the process, the easier it is to keep quality high.

Step 7: Commit Frequently

Once Codex has completed a task and you have confirmed it works, commit the changes to Git.

This is one of the best habits you can build when working with AI-assisted coding.

Commit after each successful stage. That way:

• you can roll back if a later change breaks something
• you keep a clear history of project evolution
• you avoid losing a known working version

AI can move fast, but that speed becomes dangerous if you are not preserving safe checkpoints.

Step 8: Keep ChatGPT Aware of Project Changes

After each completed task, copy the Codex log or summary of actions and paste it back into ChatGPT so it remains aware of what has changed.

This helps ChatGPT produce better follow-up prompts and more accurate troubleshooting advice.

If your project uses a database, it is also a good idea to export the current table structures and share those with ChatGPT as the project develops. That gives it a much clearer picture of your schema and reduces the chance of later prompts conflicting with your actual database design.

Handling Bugs and Design Flaws

Even with AI, you will still encounter bugs, logic problems, and design flaws.

AI does not remove debugging. It changes how you approach it.

Errors

Codex may write scripts to test your project at each stage, but that does not mean the project is error-free. It only means that the specific tests it ran passed successfully.

Real-world bugs still happen.

When you hit an error:

• take a screenshot of the error message and give it to ChatGPT
• if it is a visual or logic issue, screenshot what is going wrong
• if the bug happens over several steps, record a short video showing how to reproduce it
• upload the evidence so ChatGPT can analyse it properly

Then ask ChatGPT to:

• explain the likely cause
• suggest the fix
• produce a clean prompt for Codex to apply the change

Run the new prompt in Codex and test again.

If it does not work first time, repeat the process. AI-assisted development is still development. Iteration is normal.

Common Mistakes When Using AI to Code

AI can dramatically speed up development, but it can also accelerate bad habits if you are not careful. Some of the most common mistakes include:

1. Starting Without a Proper Plan

If you jump straight into coding without first defining the purpose, structure, stack, and data model, the project can quickly become confused. AI is fast, but speed without direction often creates technical debt.

2. Being Vague with Prompts

Weak prompts produce weak outcomes. If your instructions are unclear, incomplete, or inconsistent, the AI will fill in the gaps itself. Sometimes that works. Often it does not.

3. Letting Mission Creep Take Over

AI makes it very easy to keep adding features. That sounds good until a simple app becomes a sprawling system with too many moving parts. Define the minimum viable version first and stick to it.

4. Skipping Git Commits

This is one of the easiest mistakes to make and one of the most painful later. If AI makes several changes and one breaks the project badly, regular commits give you a safe recovery point.

5. Trusting Tests Too Much

Passing tests do not always mean the software is robust. It only means the tested scenarios passed. Edge cases, usability issues, and logic flaws may still be sitting there waiting for you.

6. Failing to Review the Code

Even if the output works, you should still look through it. AI can generate unnecessary complexity, inconsistent naming, poor security practices, or code that is difficult to maintain.

7. Not Feeding Changes Back Into ChatGPT

If ChatGPT is helping you create prompts and troubleshoot issues, it needs to remain up to date. If it is working from an outdated understanding of the project, the quality of its advice will drop.

Why This Method Works So Well

There is a real skill in writing prompts that keep Codex consistent and productive over a long build.

Most people will eventually become vague, make assumptions, or forget to mention an important constraint. Then the AI fills in the gaps, often in ways that were not intended.

That is where ChatGPT becomes extremely useful.

ChatGPT helps you:

• shape the project properly
• identify weaknesses early
• think about data structure first
• spot mission creep
• produce stronger prompts
• troubleshoot with more context
• save time and frustration

Instead of starting from a blank page every time, you are using AI to support AI.

That is the real efficiency gain.

Even if you have never coded before, you can still use ChatGPT to help you choose a sensible stack, decide on a database, understand the project structure, and create prompts for Codex to run.

That does not mean you instantly become a software engineer overnight. But it does mean the barrier to building useful software is far lower than it used to be.

If your project is simple and your goals are clear, AI may be able to produce a surprising amount of the working code for you.

One Final Warning

Always look at the code that Codex outputs.

Do this regardless of your coding ability.

Even if you are a beginner, you should still review the files, ask questions, and try to understand what has been created. AI-generated code can work and still contain weaknesses, bad practices, security issues, or unnecessary complexity.

The goal is not to hand over your thinking to AI.

The goal is to accelerate your development while staying in control.

Conclusion

The smartest way to use AI in software development is not to treat it as a magic coder that replaces thinking.

It is to use one AI tool to help you think clearly and structure the work, and another AI tool to help you implement that work quickly and systematically.

That is where the real speed boost comes from.

Used properly, ChatGPT and Codex can dramatically increase your output, reduce friction, and help you build faster than ever before.

But the real advantage does not come from typing less.

It comes from thinking better, planning better, and directing AI with clarity.

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