Best Practices Advanced

This final lesson covers everything you need to use OpenAI Codex effectively at scale: writing optimal tasks, preparing your repository, understanding security, managing costs, knowing when to use Codex vs manual coding, and scaling AI-assisted development across your team.

Writing Effective Tasks

1. One task, one concern

   Each task should focus on a single, well-defined objective. Avoid bundling unrelated changes into one task.

2. Be specific, not prescriptive

   Describe what you want done and any constraints, but let Codex decide the implementation details. Specify the "what" and "why", not the "how".

3. Include acceptance criteria

   Define what "done" looks like. List the specific tests that should pass, behaviors that should work, and edge cases to handle.

4. Reference existing patterns

   Point Codex to similar implementations in your codebase: "Follow the pattern used in src/api/users.ts" gives it a concrete example.

5. Start small, then scale

   Begin with small, low-risk tasks to build confidence. Gradually increase complexity as you learn what works well.

Repository Setup for Best Results

Optimize your repository to help Codex produce better results:

Factor	Recommendation
AGENTS.md	Create a comprehensive AGENTS.md with setup instructions, conventions, testing commands, and architectural notes
Test suite	Maintain a robust test suite. Codex validates its changes by running tests — more tests mean better validation
CI/CD	Configure CI to run on pull requests. This gives Codex (and you) confidence that changes work correctly
Linting	Use ESLint, Prettier, or similar tools. Codex follows your linting rules when they are configured
Type safety	TypeScript, type hints in Python, or other type systems help Codex understand data flow and catch errors
Documentation	Well-documented code helps Codex understand intent. JSDoc comments, README, and architecture docs all help
Clean structure	Organized file structure with clear naming conventions makes it easier for Codex to find and modify the right files

Security

Codex is designed with security as a core principle:

Cost Management

Manage your Codex usage costs effectively:

• Prioritize high-value tasks: Use Codex for tasks where the time savings justify the cost — tedious but well-defined work
• Write clear tasks: Vague tasks lead to iterations and retries, increasing cost. Invest time in clear task descriptions
• Batch similar tasks: Group related small changes into logical batches rather than submitting them one by one
• Monitor usage: Track your Codex usage through the OpenAI dashboard. Set spending limits and alerts
• Start small: Test with small, low-risk tasks before committing to large-scale usage

When to Use Codex vs Manual Coding

Use Codex	Code Manually
Well-defined bug fixes with clear reproduction steps	Complex bugs requiring deep system understanding
Adding tests for existing, untested code	Designing test architecture and strategy
Implementing features with clear requirements	Exploring architectural options and trade-offs
Routine refactoring (extract function, rename, restructure)	Fundamental architecture changes
Generating API documentation and code comments	Writing high-level design documents and ADRs
Batch processing a backlog of small tasks	Sensitive security-critical code

Scaling AI-Assisted Development

1. Start with a pilot

   Begin with one team and one repository. Document what works and what does not.

2. Establish review standards

   Create guidelines for reviewing AI-generated PRs. Define what to check and quality thresholds.

3. Standardize AGENTS.md

   Create a template AGENTS.md that teams can customize. Include organizational coding standards and conventions.

4. Track metrics

   Measure PR acceptance rate, time saved, iteration count, and cost per task. Use data to improve your process.

5. Share learnings

   Document effective task patterns, common pitfalls, and tips. Build an internal knowledge base for your team.

Frequently Asked Questions