Codex Models: AI-Powered Code Generation for Developers

OpenAI’s Codex models represent a specialized branch of GPT-3, fine-tuned specifically for code generation. Available through Azure OpenAI Service, these models can transform how developers work.

Understanding Codex

Codex is trained on billions of lines of public code. It understands:

• Multiple programming languages
• Common patterns and idioms
• Documentation and comments
• API conventions

The key models:

• code-davinci-002: Most capable, handles complex tasks
• code-cushman-001: Faster, suitable for simpler completions

Basic Code Generation

Generate code from natural language:

import openai

def generate_code(description, language="python"):
    prompt = f"""# {language.capitalize()}
# {description}

"""

    response = openai.Completion.create(
        engine="code-davinci-002",
        prompt=prompt,
        max_tokens=500,
        temperature=0,
        stop=["#", "'''", '"""']
    )

    return response.choices[0].text.strip()

# Example usage
code = generate_code(
    "Function that calculates the Fibonacci sequence up to n terms",
    "python"
)
print(code)

Output:

def fibonacci(n):
    if n <= 0:
        return []
    elif n == 1:
        return [0]
    elif n == 2:
        return [0, 1]

    fib = [0, 1]
    for i in range(2, n):
        fib.append(fib[i-1] + fib[i-2])
    return fib

Language Translation

Convert code between languages:

def translate_code(source_code, source_lang, target_lang):
    prompt = f"""# Convert the following {source_lang} code to {target_lang}

# {source_lang}:
{source_code}

# {target_lang}:
"""

    response = openai.Completion.create(
        engine="code-davinci-002",
        prompt=prompt,
        max_tokens=len(source_code) * 2,
        temperature=0
    )

    return response.choices[0].text.strip()

# Example: Python to JavaScript
python_code = """
def greet(name):
    return f"Hello, {name}!"

def main():
    print(greet("World"))
"""

js_code = translate_code(python_code, "Python", "JavaScript")
print(js_code)

Code Explanation

Get explanations for complex code:

def explain_code(code, language):
    prompt = f"""Explain what the following {language} code does in plain English:

```{language}
{code}

Explanation:"""

response = openai.Completion.create(
    engine="code-davinci-002",
    prompt=prompt,
    max_tokens=300,
    temperature=0.3
)

return response.choices[0].text.strip()

## SQL Generation

Generate SQL from natural language:

```python
def natural_language_to_sql(question, schema):
    prompt = f"""### Database Schema:
{schema}

### Question: {question}

### SQL Query:
SELECT"""

    response = openai.Completion.create(
        engine="code-davinci-002",
        prompt=prompt,
        max_tokens=200,
        temperature=0,
        stop=[";", "--"]
    )

    return "SELECT" + response.choices[0].text + ";"

# Example
schema = """
Tables:
- customers (id, name, email, created_at)
- orders (id, customer_id, total, status, created_at)
- products (id, name, price, category)
- order_items (id, order_id, product_id, quantity)
"""

sql = natural_language_to_sql(
    "Find all customers who have placed more than 5 orders this month",
    schema
)
print(sql)

Unit Test Generation

Automatically generate tests:

def generate_tests(function_code, language="python"):
    prompt = f"""Write unit tests for the following {language} function:

```{language}
{function_code}

Unit tests using pytest:

import pytest
"""

    response = openai.Completion.create(
        engine="code-davinci-002",
        prompt=prompt,
        max_tokens=500,
        temperature=0.2
    )

    return "import pytest\n" + response.choices[0].text

# Example
func = """
def calculate_discount(price, discount_percent):
    if discount_percent < 0 or discount_percent > 100:
        raise ValueError("Discount must be between 0 and 100")
    return price * (1 - discount_percent / 100)
"""

tests = generate_tests(func)
print(tests)

Code Review Assistant

Get automated code review suggestions:

def review_code(code, language):
    prompt = f"""Review the following {language} code for:
1. Bugs or potential issues
2. Performance improvements
3. Security concerns
4. Best practice violations

Code:
```{language}
{code}

Code Review:"""

response = openai.Completion.create(
    engine="code-davinci-002",
    prompt=prompt,
    max_tokens=500,
    temperature=0.3
)

return response.choices[0].text.strip()

## Building a CLI Tool

Create a command-line code assistant:

```python
#!/usr/bin/env python3
import argparse
import openai
import sys
import os

openai.api_type = "azure"
openai.api_base = os.environ["AZURE_OPENAI_ENDPOINT"]
openai.api_version = "2022-03-01-preview"
openai.api_key = os.environ["AZURE_OPENAI_KEY"]

def main():
    parser = argparse.ArgumentParser(description="AI Code Assistant")
    parser.add_argument("action", choices=["generate", "explain", "test", "review"])
    parser.add_argument("--language", "-l", default="python")
    parser.add_argument("--file", "-f", help="Input file")
    parser.add_argument("--prompt", "-p", help="Natural language prompt")

    args = parser.parse_args()

    if args.file:
        with open(args.file, 'r') as f:
            code = f.read()
    else:
        code = sys.stdin.read() if not sys.stdin.isatty() else None

    if args.action == "generate":
        if not args.prompt:
            print("Error: --prompt required for generate")
            sys.exit(1)
        result = generate_code(args.prompt, args.language)
    elif args.action == "explain":
        if not code:
            print("Error: Provide code via --file or stdin")
            sys.exit(1)
        result = explain_code(code, args.language)
    elif args.action == "test":
        if not code:
            print("Error: Provide code via --file or stdin")
            sys.exit(1)
        result = generate_tests(code, args.language)
    elif args.action == "review":
        if not code:
            print("Error: Provide code via --file or stdin")
            sys.exit(1)
        result = review_code(code, args.language)

    print(result)

if __name__ == "__main__":
    main()

Usage:

# Generate code
./code_assistant.py generate -l python -p "REST API client for weather data"

# Explain code
cat my_script.py | ./code_assistant.py explain -l python

# Generate tests
./code_assistant.py test -f my_module.py

# Review code
./code_assistant.py review -f suspicious_code.py

Integration with VS Code

Create a VS Code extension that uses Codex:

// extension.ts
import * as vscode from 'vscode';
import axios from 'axios';

export function activate(context: vscode.ExtensionContext) {
    let disposable = vscode.commands.registerCommand(
        'codex-assistant.generateCode',
        async () => {
            const editor = vscode.window.activeTextEditor;
            if (!editor) return;

            const prompt = await vscode.window.showInputBox({
                prompt: 'Describe what code you want to generate'
            });

            if (!prompt) return;

            const language = editor.document.languageId;

            try {
                const response = await axios.post(
                    `${process.env.AZURE_OPENAI_ENDPOINT}/openai/deployments/code-davinci-002/completions`,
                    {
                        prompt: `# ${language}\n# ${prompt}\n\n`,
                        max_tokens: 500,
                        temperature: 0
                    },
                    {
                        headers: {
                            'api-key': process.env.AZURE_OPENAI_KEY,
                            'Content-Type': 'application/json'
                        }
                    }
                );

                const generatedCode = response.data.choices[0].text;
                editor.edit(editBuilder => {
                    editBuilder.insert(editor.selection.active, generatedCode);
                });
            } catch (error) {
                vscode.window.showErrorMessage('Failed to generate code');
            }
        }
    );

    context.subscriptions.push(disposable);
}

Best Practices

1. Validate Generated Code

Never execute generated code without review:

def safe_execute(generated_code, allowed_modules=None):
    # Parse the AST to check for dangerous operations
    import ast

    tree = ast.parse(generated_code)

    for node in ast.walk(tree):
        # Check for imports
        if isinstance(node, ast.Import):
            for alias in node.names:
                if allowed_modules and alias.name not in allowed_modules:
                    raise SecurityError(f"Disallowed import: {alias.name}")

        # Check for dangerous calls
        if isinstance(node, ast.Call):
            if isinstance(node.func, ast.Name):
                if node.func.id in ['eval', 'exec', 'compile', '__import__']:
                    raise SecurityError(f"Dangerous function: {node.func.id}")

    # If validation passes, consider execution
    return True

2. Use Stop Sequences

Prevent runaway generation:

response = openai.Completion.create(
    engine="code-davinci-002",
    prompt=prompt,
    max_tokens=500,
    stop=[
        "\n\n\n",  # Multiple blank lines
        "# End",    # Custom end marker
        "```",      # Markdown code block end
        "if __name__"  # Module entry point
    ]
)

3. Temperature for Different Tasks

Task	Temperature	Rationale
SQL generation	0	Deterministic, exact
Bug fixes	0-0.1	Precise corrections
Code completion	0.2	Some flexibility
Test generation	0.3	Varied test cases
Creative coding	0.5-0.7	Novel approaches

Limitations

Codex isn’t perfect:

1. Outdated knowledge: Training data has a cutoff date
2. Hallucinated APIs: May invent non-existent functions
3. Security blind spots: Generated code may have vulnerabilities
4. Context limits: Can’t understand your entire codebase
5. License concerns: Output may resemble training data

Always review, test, and validate generated code.

Conclusion

Codex models are productivity multipliers, not replacements for developers. Use them to:

• Accelerate boilerplate code creation
• Get starting points for complex algorithms
• Learn new languages and frameworks
• Generate test cases
• Document existing code

The key is treating Codex as a capable but fallible assistant that still requires human oversight.

Resources

• OpenAI Codex Documentation
• GitHub Copilot (powered by Codex)
• Azure OpenAI Codex Models