Claude for Code Generation

Overview

Using Claude to generate, review, and refactor production code. AI-powered coding tools are transforming software development workflows.

Setup

bash
Install required packages
pip install openai anthropic python-dotenv
Set API keys
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

Core Workflow

python
from openai import OpenAI
from anthropic import Anthropic
from pathlib import Path
openai_client = OpenAI()
claude_client = Anthropic()
class AICodeAssistant:
    """Claude for Code Generation implementation."""
    
    SYSTEM_PROMPT = """You are an expert software engineer with deep knowledge of:
Software architecture and design patterns
Code quality, testing, and maintainability
Security best practices
Performance optimization
Always:
Write clean, idiomatic code with clear variable names
Include error handling and edge cases
Add type hints (for Python) or JSDoc (for JavaScript)
Provide brief explanations of key decisions
Consider security implications"""
    
    def __init__(self, model: str = "gpt-4o"):
        self.client = OpenAI()
        self.model = model
    
    def generate(self, prompt: str, language: str = "Python", **kwargs) -> str:
        """Generate code with AI assistance."""
        
        user_message = f"""Language: {language}
Task: {prompt}
Please provide:
Complete, working code
Brief inline comments for complex logic
Example usage
Any important caveats or alternatives"""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": self.SYSTEM_PROMPT},
                {"role": "user", "content": user_message}
            ],
            temperature=0.1,
            max_tokens=3000
        )
        
        return response.choices[0].message.content
    
    def review(self, code: str, focus: str = "general") -> str:
        """Review code and suggest improvements."""
        
        prompt = f"""Review this code with focus on: {focus}

{code}

Provide:
Overall assessment (1-10 score)
Issues found (if any)
Specific improvements
Refactored version if improvements are significant"""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": self.SYSTEM_PROMPT},
                {"role": "user", "content": prompt}
            ],
            temperature=0.1,
            max_tokens=3000
        )
        
        return response.choices[0].message.content
    
    def explain(self, code: str, audience: str = "intermediate") -> str:
        """Explain code in plain language."""
        
        prompt = f"""Explain this code for a {audience} developer:

{code}

Include:
What the code does (overview)
How it works (step by step)
Key concepts used
Potential gotchas"""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.3,
            max_tokens=1500
        )
        
        return response.choices[0].message.content
Usage examples
assistant = AICodeAssistant()
Generate code
code = assistant.generate(
    "Create a rate limiter class using token bucket algorithm",
    language="Python"
)
print("Generated code:")
print(code)
Review existing code
review = assistant.review(
    code="def get_user(id): return db.query(f'SELECT * FROM users WHERE id={id}')",
    focus="security"
)
print("\nCode review:")
print(review)

Advanced Patterns

Code Context Window

python
def generate_with_context(
    task: str,
    existing_files: dict[str, str],
    language: str = "Python"
) -> str:
    """Generate code with full project context."""
    
    context_str = "\n\n".join([
        f"File: {name}\n

\n{content[:2000]}\n

"
        for name, content in existing_files.items()
    ])
    
    prompt = f"""Project context:
{context_str}
New task: {task}
Language: {language}
Generate code that integrates with the existing codebase."""
    
    response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.1,
        max_tokens=2000
    )
    
    return response.choices[0].message.content

Multi-Language Support

python
def translate_code(source_code: str, from_lang: str, to_lang: str) -> str:
    """Translate code between programming languages."""
    
    response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "user",
            "content": f"Translate this {from_lang} code to idiomatic {to_lang}:\n\n

{from_lang}\n{source_code}\n

\n\nProvide {to_lang} equivalent:"
        }],
        temperature=0.1,
        max_tokens=2000
    )
    
    return response.choices[0].message.content
Example: Python to TypeScript
ts_code = translate_code(
    source_code="def calculate_fibonacci(n: int) -> int:\n    if n <= 1: return n\n    return calculate_fibonacci(n-1) + calculate_fibonacci(n-2)",
    from_lang="Python",
    to_lang="TypeScript"
)
print(ts_code)

Integration in Development Workflow

bash
Pre-commit hook for AI code review
#!/bin/bash
.git/hooks/pre-commit
changed_files=$(git diff --cached --name-only --diff-filter=ACM | grep '.py$')
if [ -n "$changed_files" ]; then
    echo "Running AI code review..."
    python scripts/ai_review.py $changed_files
fi

Best Practices

Be specific — detailed prompts give better code

Provide context — share relevant existing code

Iterate — use follow-up prompts to refine

Verify output — always test generated code

Learn patterns — understand why AI made choices

Resources

OpenAI Codex: https://platform.openai.com/docs

GitHub Copilot: https://github.com/features/copilot

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