提示工程高级技巧学习指南

def ab_test_prompts(prompt_a, prompt_b, test_cases):
    results = {'prompt_a': [], 'prompt_b': []}
    
    for test_case in test_cases:
        # 使用提示词A
        response_a = llm.generate(prompt_a.format(input=test_case))
        results['prompt_a'].append(evaluate_response(response_a, test_case))
        
        # 使用提示词B
        response_b = llm.generate(prompt_b.format(input=test_case))
        results['prompt_b'].append(evaluate_response(response_b, test_case))
    
    # 计算平均分
    avg_a = sum(results['prompt_a']) / len(results['prompt_a'])
    avg_b = sum(results['prompt_b']) / len(results['prompt_b'])
    
    return {
        'prompt_a_score': avg_a,
        'prompt_b_score': avg_b,
        'winner': 'prompt_a' if avg_a > avg_b else 'prompt_b'
    }

class PromptVersionControl:
    def __init__(self):
        self.prompts = {}
        self.versions = {}
    
    def add_prompt(self, name, content, description=""):
        """添加新的提示词"""
        if name not in self.prompts:
            self.prompts[name] = []
        
        version = len(self.prompts[name]) + 1
        self.prompts[name].append({
            'version': version,
            'content': content,
            'description': description,
            'created_at': datetime.now(),
            'metrics': {}
        })
        
        # 设置为当前版本
        self.versions[name] = version
        
        return version

def optimize_prompt(initial_prompt, eval_fn, iterations=10, population_size=5):
    # 初始化种群
    population = [initial_prompt]
    
    # 生成初始变异
    for _ in range(population_size - 1):
        mutated = mutate_prompt(initial_prompt)
        population.append(mutated)
    
    for i in range(iterations):
        print(f"迭代 {i+1}/{iterations}")
        
        # 评估种群
        scores = [(prompt, eval_fn(prompt)) for prompt in population]
        scores.sort(key=lambda x: x[1], reverse=True)
        
        # 选择父代和生成新一代...
    
    # 返回最佳提示词
    best_prompt = max([(prompt, eval_fn(prompt)) for prompt in population], key=lambda x: x[1])
    return best_prompt