The current OpenAI Codex cannot be directly fine-tuned by individual users in the traditional sense of machine learning model customization. Unlike some AI models that allow users to provide additional training data to specialize the model for specific tasks, Codex operates as a pre-trained system that users interact with through prompts and instructions rather than model modification. However, there are several effective ways to customize Codex’s behavior and output for specific tasks and organizational needs. The most important method is through detailed prompting and the use of AGENTS.md files, which are configuration files that you can place in your repository to guide how Codex should approach your specific project, coding standards, and development practices.
AGENTS.md files serve as project-specific instruction sets that tell Codex how to work with your particular codebase. These files can specify your preferred coding conventions, testing frameworks, deployment procedures, architectural patterns, and any other project-specific requirements. For example, you might specify that all functions should include comprehensive docstrings, that certain security patterns must be followed, or that specific testing procedures should be used. Codex reads and follows these instructions throughout its work on your project, effectively customizing its behavior without modifying the underlying model. You can also provide detailed context about your project’s structure, dependencies, and requirements through these configuration files, helping Codex understand your specific domain and generate more appropriate solutions.
Beyond configuration files, you can influence Codex’s behavior through iterative feedback and refinement within sessions. When Codex generates code that doesn’t quite meet your requirements, you can provide specific feedback about what needs to be changed, and the system will adjust its approach accordingly. This feedback helps Codex understand your preferences and requirements for the current task, though this customization doesn’t persist beyond the current session. For organizations with specific needs, enterprise versions of Codex may offer additional customization options, and teams can develop internal guidelines and prompting strategies that consistently produce results aligned with their standards. While this isn’t traditional fine-tuning, these approaches allow teams to effectively customize Codex’s output to match their specific requirements and development practices.