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Using ChatGPT Codex for Development

ChatGPT Codex blends powerful code generation with conversational guidance. This lesson shows how to use it productively in your development workflow—covering where it shines, where to be cautious, how to choose between model versions, and how to involve it in creating pull requests.

Why Use Codex?

Codex isn’t just a code generator—it’s a collaborative tool that accelerates software development by offloading repetitive tasks and supporting technical communication.

Here’s where it adds the most value:

  • Rapid prototyping: Generate boilerplate code or early drafts of logic to explore design options quickly.
  • Documentation generation: Automatically produce docstrings, inline comments, and even README files to improve clarity.
  • Code interpretation: Break down unfamiliar code into plain-language summaries for onboarding or debugging.
  • Multilingual support: Works across a wide range of programming languages and frameworks.

These strengths make Codex especially useful for early-stage development, refactoring, and improving code comprehension.

When to Be Cautious

Codex is a productivity multiplier—but not a substitute for engineering judgment. Key limitations include:

  • Risk of subtle bugs: Even when code looks correct, logic errors or edge cases may go undetected. Always test thoroughly.
  • No full-project awareness: It operates within the context of your prompt, not your entire codebase—so architectural alignment isn’t guaranteed.
  • Security oversights: Generated code may not conform to your organization’s security standards or threat models.

Use Codex to move faster—but not to skip validation.

Choosing the Right Model

Different Codex models serve different needs. Here’s how to decide:

Model Best For Considerations
gpt-4 Complex algorithms, nuanced explanations Most accurate, but slower and more costly
gpt-3.5-turbo Iterative tasks, quick feedback loops Faster and cheaper, but less reliable for edge cases

When speed matters more than depth, use gpt-3.5-turbo. For critical logic or architectural planning, prefer gpt-4.

Always check OpenAI’s documentation for current version limits, pricing, and features.

Integrating Codex into Pull Request Workflows

Codex can assist at every stage of a pull request (PR)—from initial code generation to refining documentation. Here’s a typical workflow:

flowchart TD
    A[Write prompt describing code change] --> B[Generate code with Codex]
    B --> C[Review and test locally]
    C --> D[Commit to feature branch]
    D --> E[Open pull request]
    E --> F[Use Codex to refine PR description]
    F --> G[Team review and merge]

Step-by-Step Breakdown

  1. Write a clear prompt: Describe the change in functional terms. Mention inputs, outputs, and any constraints.
  2. Generate and review code: Let Codex produce the first pass, then inspect it for logic, security, and style.
  3. Test and document: Add validation and explanatory comments where needed.
  4. Commit and open a PR: Follow your team’s version control practices.
  5. Refine the PR with Codex: Use it to generate a summary, clarify technical details, or outline open questions.
  6. Collaborate for merge: Codex helps with prep—but peer review finalizes the decision.

Final Thoughts

Codex is most powerful when treated as a creative partner, not an oracle. It accelerates tedious work and enhances communication—but always needs human oversight.

Use it to free up cognitive space for deeper engineering thinking.

Follow-Up Exercises

Continue exploring LLM tooling by building your own utilities:

  • Prompt Test Harness – implement a script for quickly iterating on prompts and saving responses.
  • API Test Harness – scaffold a command-line tool to send HTTP requests and capture logs.

Treating Codex as a collaborative partner speeds up prototyping and documentation. Building on Lesson 04 – Understanding response_format={'type': 'json_object'}, which underscored the importance of structured outputs, Lesson 06 – Best Practices for Using Codex Actions in Parallel will show you how to coordinate multiple AI-driven edits without conflict.