OpenAI Codex FAQ

OpenAI Codex is an AI model developed by OpenAI that translates natural language into code...

Codex works by understanding the intent behind your natural language descriptions and generating corresponding code in various programming languages. It can:

• Convert natural language descriptions into executable code
• Complete partial code snippets
• Explain existing code functionality
• Suggest code improvements
• Generate technical documentation

Learn more about OpenAI Codex in the official introduction

While the original Codex API has been discontinued, you can still access similar functionality through:

• GitHub Copilot: The most direct way to use Codex technology, available as an extension for popular IDEs like VS Code, Visual Studio, and JetBrains IDEs
• OpenAI API: Use GPT-3.5 or GPT-4 models via the Chat Completions API, which can generate code with proper prompting
• OpenAI Codex CLI: A command-line tool that brings AI coding capabilities to your terminal

To get started with GitHub Copilot:

1. Visit github.com/features/copilot
2. Sign up for a subscription (free trial available)
3. Install the extension for your preferred IDE
4. Start coding and see AI-powered suggestions appear as you type

View the GitHub Copilot quickstart guide | Learn about OpenAI Codex CLI

The standalone OpenAI Codex API was discontinued in March 2023. OpenAI recommended that developers transition to using GPT-3.5 Turbo or GPT-4 models, which also have code generation capabilities and in some ways surpass the original Codex model.

However, the technology behind Codex lives on through:

• GitHub Copilot, which is built on Codex technology
• OpenAI's newer models (GPT-3.5, GPT-4) that have strong code generation abilities
• Azure OpenAI Service, which offered Codex models for a transitional period
• OpenAI Codex CLI, which provides a command-line interface for AI-assisted coding

For new users looking to leverage AI for code generation, GitHub Copilot is currently the most accessible and direct implementation of Codex technology.

Learn about GPT-3.5 Turbo | Learn about GPT-4

OpenAI Codex was trained on a wide variety of programming languages and can generate code in many of them, including:

• Python (strongest support)
• JavaScript
• TypeScript
• Go
• Ruby
• PHP
• Java
• C#
• C++
• Shell scripts
• SQL

Python has the strongest support, likely due to its prevalence in the training data. The successor models like GPT-3.5 and GPT-4 have inherited this multi-language capability and expanded support for some languages.

For beginners, starting with Python is often recommended as it tends to produce the most accurate and reliable code generation results.

Learn more about Codex language support

While powerful, Codex and similar AI code generation tools have several limitations that beginners should be aware of:

• Code quality issues: Generated code may contain bugs, inefficiencies, or security vulnerabilities that beginners might not recognize
• Context limitations: The model can only process a limited amount of context, making it challenging for understanding large codebases
• Hallucinations: Sometimes the AI might generate code that looks plausible but doesn't actually work or references non-existent libraries
• Learning curve: Effectively prompting the AI to generate useful code requires skill and practice
• Outdated knowledge: The model may not be aware of the latest libraries, frameworks, or best practices

For beginners, it's important to:

• Always review and understand the generated code before using it
• Test the code thoroughly to ensure it works as expected
• Use AI as a learning tool, not just a code generator
• Verify security and performance aspects of the generated code

Read GitHub Copilot best practices

GitHub Copilot is actually built on OpenAI Codex technology, but there are several key differences:

• Purpose and integration: Copilot is an end-user product directly integrated into IDEs (like VS Code, Visual Studio, etc.), while Codex was the underlying API and technology
• Accessibility: Copilot is a commercial product requiring a subscription, while the Codex API was previously available to developers but has been discontinued
• User interface: Copilot provides a more user-friendly interface with inline suggestions, while Codex as an API offered more raw functionality
• Training data: Copilot may have additional training and optimization beyond the base Codex model, particularly for GitHub repositories
• Features: Copilot includes features like test generation, explanation of code, and multi-line completions in a more integrated way

For beginners, GitHub Copilot offers a much more accessible entry point to AI-assisted coding than the raw Codex API ever did, with a focus on productivity within familiar development environments.

Read the GitHub Copilot introduction | Learn what GitHub Copilot can do

With the discontinuation of the Codex API, several excellent alternatives have emerged that are particularly suitable for beginners:

• GitHub Copilot: The most direct successor to Codex, offering IDE integration and real-time code suggestions
• Amazon CodeWhisperer: Amazon's AI coding assistant with free tier options for individual developers
• Tabnine: A code completion tool with both free and paid tiers that works across multiple IDEs
• Replit Ghostwriter: Built into the Replit platform, making it ideal for beginners who prefer browser-based coding
• Google Gemini Code Assist: Google's AI code generation and completion tool
• Codeium: An open-source AI coding assistant with a free tier
• ChatGPT: While not specifically designed for coding, the free version can generate code snippets and explain programming concepts

For absolute beginners, GitHub Copilot and Replit Ghostwriter are often recommended due to their user-friendly interfaces and integration with development environments that are already beginner-friendly.

Try GitHub Copilot | Explore Amazon CodeWhisperer | Learn about Replit Ghostwriter

Since the original OpenAI Codex API has been discontinued, you can't directly purchase access to it anymore. However, you can access similar functionality through these options with their associated costs:

• GitHub Copilot: Individual developers $10/month or $100/year; Students/OSS maintainers free; Business $19/user/month; Enterprise custom
• OpenAI API (GPT models): GPT-3.5 $0.0005/1K input tokens, $0.0015/1K output; GPT-4 $0.03/1K input, $0.06/1K output; free credit available
• Alternative services: CodeWhisperer free for individuals (paid for business); Tabnine free + Pro $12/month; Codeium free for individuals, team plans from $12/user/month

For beginners, starting with free tiers (e.g., CodeWhisperer, ChatGPT free) is a cost-effective way to explore before paying.

View GitHub Copilot plans | Check OpenAI API pricing

While Codex itself is no longer available, these tips apply to any AI code generation tool (GitHub Copilot, GPT models, etc.) and can significantly improve the quality of generated code:

• Be specific in your prompts: Clearly describe what you want the code to do, including input/output examples, edge cases, and performance requirements
• Provide context: Include relevant information about your project, such as the programming language, frameworks, and libraries you're using
• Use iterative refinement: Start with a basic implementation and then ask for improvements or optimizations
• Ask for explanations: Request that the AI explain how the code works, which can help identify potential issues
• Specify coding standards: Mention any specific coding standards or patterns you want the generated code to follow
• Request test cases: Ask the AI to generate test cases along with the implementation code

Example of a good prompt: "Write a Python function that finds the longest palindromic substring in a given string. The function should handle empty strings and strings with no palindromes. Use efficient algorithms and include comments explaining the approach. Also provide a simple test case."

Remember that AI-generated code should always be reviewed and tested before being used in production environments.

Learn prompt engineering for GitHub Copilot | Read OpenAI's prompt engineering guide

When using Codex-like AI code generation tools, beginners should be aware of several important security and ethical considerations:

• Security vulnerabilities: AI-generated code may contain security flaws or not follow best security practices, potentially introducing vulnerabilities into your applications
• Intellectual property concerns: Generated code might resemble existing copyrighted code, potentially raising copyright issues
• Over-reliance: Becoming too dependent on AI for coding can hinder the development of your own programming skills and understanding
• Data privacy: Code you submit to AI services might be stored and used for model training, so avoid sharing sensitive or proprietary code
• Responsibility: You are ultimately responsible for any code you use, even if it was generated by AI

Best practices to mitigate these risks include:

• Always review and understand AI-generated code before using it
• Run security scanning tools on generated code
• Test thoroughly, especially for edge cases
• Use AI as a learning tool by understanding why the code works
• Be familiar with the terms of service of the AI tools you use
• Consider using tools like GitHub Copilot that can be configured to respect your privacy settings

Read about responsible use of GitHub Copilot | Learn about common security vulnerabilities (OWASP)