Code Generation and Development Assistance with LLMs

Introduction

Language models can be used to generate code and assist developers in their day-to-day work. This tutorial explores the different ways to implement these capabilities with LLMs.

Types of Development Assistance

1. Code Generation

Function generation
Class generation
Test generation
Documentation generation
Boilerplate code generation

2. Refactoring and Optimization

Code refactoring
Performance optimization
Bug fixing
Readability improvements
Code modernization

3. Design Assistance

Architecture suggestions
Best practices
Design patterns
Algorithmic optimizations
Security and performance

Implementation

1. Code Generator

from openai import OpenAI
import json
from typing import List, Dict, Any
import numpy as np

class CodeGenerator:
    def __init__(self, api_key: str):
        self.client = OpenAI(api_key=api_key)
    
    def generate_code(self, requirements: Dict[str, Any], 
                     language: str = "python") -> Dict[str, Any]:
        prompt = self._create_generation_prompt(requirements, language)
        
        response = self.client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "Tu es un expert en génération de code."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.3
        )
        
        return json.loads(response.choices[0].message.content)
    
    def _create_generation_prompt(self, requirements: Dict[str, Any], 
                                language: str) -> str:
        return f"""
        Génère du code en {language} selon les exigences suivantes :
        
        Exigences : {json.dumps(requirements, indent=2)}
        
        Fournis :
        1. Le code généré
        2. Les explications
        3. Les tests unitaires
        4. La documentation
        
        Réponds au format JSON.
        """