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Unlock 10 ChatGPT Codex Secrets After 60 Hours of Pair Programming

▼ Summary

– Step-by-step development with incremental instructions produces more robust results than providing full specifications all at once to AI coding assistants.
AI tools can rapidly destroy codebases with misinterpreted prompts, making regular backups and using undo features essential safety practices.
– Maintaining project stability requires techniques like AGENTS.md files for standardized instructions, screenshots for visual guidance, and regular code refactoring.
– Effective AI pair programming involves separating UI creation, styling, and functionality into distinct phases rather than attempting complete implementations simultaneously.
– Session management is crucial, including using handover notes between sessions and explicitly requesting maintainable code with proper formatting and comments.

Spending more than sixty hours in intensive pair programming sessions with ChatGPT Codex revealed powerful strategies for maximizing this AI coding tool’s potential. This deep dive into collaborative development uncovered techniques that dramatically boost productivity while minimizing the risks inherent in AI-assisted programming. The experience yielded a major feature upgrade for a core product alongside the creation of four new add-ons, representing one of the most prolific development periods ever undertaken.

The traditional concept of pair programming, where two developers work side-by-side, has been redefined. Now, it encompasses a human developer partnering with an artificial intelligence. This new dynamic requires a different approach than coding alone or with another person. The initial dozen hours using the $20 monthly ChatGPT Plus plan resulted in three service interruptions due to resource overuse. Switching to the $200 ChatGPT Pro subscription allowed for an uninterrupted forty-five hour marathon, proving essential for sustained, complex project work.

Building applications step-by-step proved far more effective than providing full specifications upfront. Attempting to hand the AI a complete product requirements document (PRD) led to failure on multiple occasions. The AI would misinterpret a single instruction, leading to a cascade of errors that were difficult to untangle. Instead, a methodical, layered approach yielded robust, workable results.

The process began by describing only the desired user interface elements, explicitly instructing the AI not to wire up any functionality or styling. Once all elements were on the page, the next step involved directing the AI to position and style them, a task particularly well-suited for handling finicky CSS. Only after the layout was perfect did work begin on the local JavaScript interactions. The final phase involved implementing the core business logic. This sequential method made the entire build process manageable.

It is crucial to remember that AIs can destroy code as quickly as they create it. Their incredible speed is a double-edged sword. A single poorly worded prompt can wreak havoc on an entire codebase. This reality underscores the importance of making small, incremental changes rather than asking the AI to take large, sweeping actions. Regularly backing up or committing changes provides a safety net for when things go wrong.

Leveraging the Undo button is a non-negotiable best practice. It completely reverses the AI’s last set of actions, serving as an instant recovery tool. Furthermore, developers should resist the AI’s tendency to suggest adding business logic prematurely. While it’s tempting to follow these suggestions, it’s better to note them for later implementation once the core structure is solid.

Implementing an AGENTS.md file in the project root provides a set of persistent, standardized instructions. This file can contain directives like prohibiting modifications to library directories, ensuring external dependencies remain updateable without conflict. This eliminates the need to repeat fundamental rules at the start of every session.

Using screenshots extensively bridges comprehension gaps. When textual descriptions fail, a simple screenshot with arrows pointing to areas of focus often gives the AI the visual context it needs to understand the task. Similarly, copying HTML and CSS directly from a browser’s inspector tool and pasting it into the chat helps the AI accurately identify and manipulate page elements.

Session management is another critical area. To avoid hitting context window limits after pasting large code blocks or error logs, instruct the AI to generate a session handover summary. This summary can be pasted into a new session to quickly bring the AI up to speed, mimicking an end-of-shift report.

Codex has a tendency to create monolithic files. Instructing the AI to refactor its code improves long-term maintainability. This involves directing it to move inline JavaScript and CSS into separate files within organized folders and to group related functions into logical category files.

A final, bonus insight is that the AI does not automatically code for human readability. You must explicitly ask it to format code, add comments, and provide explanations. When prompted, it will produce well-documented, structured code that is much easier for developers to understand and modify later.

The journey of pairing with an AI like Codex is one of collaboration and clear communication. By breaking down projects, managing sessions wisely, and using tools like AGENTS.md and screenshots, developers can harness the AI’s power while maintaining control over the final product.

(Source: ZDNET)

Topics

ai pair programming 95% stepwise development 93% ai limitations 90% project management 88% code refactoring 87% error handling 85% ui development 83% session management 82% documentation standards 80% visual guidance 78%