AI创业公司提示系统市场评估案例：从0到1的突破（架构师拆解）

    timeline
    title AI交互范式演进
    section 规则驱动时代
        1950s : 图灵测试提出，早期符号系统
        1970s : 专家系统兴起 (MYCIN, DENDRAL)
        1980s : 知识工程繁荣与AI寒冬
    section 数据驱动时代
        2010s : 深度学习革命，监督学习主导
        2012 : AlexNet引发计算机视觉突破
        2015 : seq2seq模型推动NLP发展
        2018 : BERT开创预训练语言模型时代
    section 提示驱动时代
        2020 : GPT-3展示少样本学习能力
        2021 : In-context Learning理论奠基
        2022 : ChatGPT引爆提示工程关注
        2023 : 提示系统商业化加速，工具链爆发
        2024+ : 提示工程与AI Agent融合
    
    
    mermaid
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/IxbDt5UrN73CdMHePu9nAjhB8Js6.png)

    from jinja2 import Template, Environment, meta
    from typing import Dict, List, Any, Optional, Set
    import re
    import logging
    
    class PromptTemplateEngine:
    def __init__(self):
        self.env = Environment(trim_blocks=True, lstrip_blocks=True)
        self.logger = logging.getLogger(__name__)
        
    def extract_variables(self, template_str: str) -> Set[str]:
        """提取模板中的变量，用于验证输入数据"""
        try:
            parsed_content = self.env.parse(template_str)
            return meta.find_undeclared_variables(parsed_content)
        except Exception as e:
            self.logger.error(f"Error extracting variables: {str(e)}")
            return set()
    
    def validate_input(self, template_str: str, input_data: Dict[str, Any]) -> bool:
        """验证输入数据是否包含模板所需的所有变量"""
        required_vars = self.extract_variables(template_str)
        provided_vars = set(input_data.keys())
        missing_vars = required_vars - provided_vars
        if missing_vars:
            self.logger.warning(f"Missing variables: {missing_vars}")
            return False
        return True
    
    def render(self, 
              template_str: str, 
              input_data: Dict[str, Any],
              strict: bool = True) -> Optional[str]:
        """
        渲染提示模板
        
        Args:
            template_str: 提示模板字符串
            input_data: 用于填充模板的变量数据
            strict: 若为True，缺少变量时抛出异常；否则使用默认值
            
        Returns:
            渲染后的提示字符串，或None（出错时）
        """
        try:
            # 非严格模式下，为缺失变量提供默认值
            if not strict:
                required_vars = self.extract_variables(template_str)
                for var in required_vars:
                    input_data.setdefault(var, f"{{{{ {var} }}}}")
            
            template = self.env.from_string(template_str)
            return template.render(**input_data)
        except Exception as e:
            self.logger.error(f"Template rendering error: {str(e)}")
            return None
    
    def escape_special_characters(self, text: str) -> str:
        """转义可能干扰模板引擎的特殊字符"""
        return re.sub(r'{{|}}', lambda m: f'{{{{"{{" if m.group(0) == "}}" else "}}"}}}}', text)
    
    def create_variant(self, 
                      base_template: str, 
                      modifications: Dict[str, str]) -> str:
        """基于基础模板创建变体，用于A/B测试"""
        variant = base_template
        for placeholder, value in modifications.items():
            variant = variant.replace(f"{{{{ {placeholder} }}}}", value)
        return variant
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/vLJ5rZ8Oj4bxPhVlKFBzo9SEcyTG.png)

    from typing import Dict, List, Callable, Optional, Any, Tuple
    import time
    import numpy as np
    import pandas as pd
    from sklearn.metrics import accuracy_score, f1_score
    import logging
    import asyncio
    from dataclasses import dataclass
    
    @dataclass
    class EvaluationResult:
    """提示评估结果数据类"""
    accuracy: Optional[float] = None
    f1_score: Optional[float] = None
    latency: Optional[float] = None
    token_usage: Optional[int] = None
    cost: Optional[float] = None
    robustness_score: Optional[float] = None
    user_rating: Optional[float] = None
    evaluation_time: float = time.time()
    
    def to_dict(self) -> Dict[str, Any]:
        return {k: v for k, v in self.__dict__.items() if v is not None}
    
    class PromptEvaluator:
    def __init__(self, 
                 model_client: Any,
                 metrics: Optional[List[str]] = None,
                 cost_per_token: float = 0.000002,  # 每token成本（美元）
                 logger: Optional[logging.Logger] = None):
        """
        提示评估器初始化
        
        Args:
            model_client: LLM模型客户端，需实现generate方法
            metrics: 要评估的指标列表
            cost_per_token: 每token的成本，用于计算总费用
            logger: 日志记录器
        """
        self.model_client = model_client
        self.default_metrics = ["accuracy", "latency", "token_usage", "cost"]
        self.metrics = metrics if metrics else self.default_metrics
        self.cost_per_token = cost_per_token
        self.logger = logger or logging.getLogger(__name__)
        
        # 注册评估指标函数
        self.metric_functions = {
            "accuracy": self._evaluate_accuracy,
            "f1_score": self._evaluate_f1,
            "latency": self._evaluate_latency,
            "token_usage": self._evaluate_token_usage,
            "cost": self._evaluate_cost,
            "robustness_score": self._evaluate_robustness
        }
    
    def evaluate(self, 
                prompt: str, 
                test_cases: List[Dict[str, Any]],
                reference_outputs: Optional[List[str]] = None) -> EvaluationResult:
        """
        评估提示在测试集上的性能
        
        Args:
            prompt: 要评估的提示字符串
            test_cases: 测试用例列表，每个用例是包含输入数据的字典
            reference_outputs: 参考输出，用于计算准确性等指标
            
        Returns:
            评估结果对象
        """
        result = EvaluationResult()
        
        # 验证输入
        if "accuracy" in self.metrics or "f1_score" in self.metrics:
            if not reference_outputs or len(test_cases) != len(reference_outputs):
                self.logger.warning("Missing or mismatched reference outputs for accuracy metrics")
                self.metrics = [m for m in self.metrics if m not in ["accuracy", "f1_score"]]
        
        # 执行测试用例并收集结果
        predictions = []
        latencies = []
        token_usages = []
        
        for i, test_case in enumerate(test_cases):
            # 如果测试用例是模板化的，渲染提示
            if isinstance(test_case, dict) and "input" in test_case:
                rendered_prompt = prompt.format(**test_case["input"])
            else:
                rendered_prompt = prompt
            
            # 记录执行时间
            start_time = time.time()
            
            # 调用模型
            try:
                response = self.model_client.generate(rendered_prompt)
                output = response["choices"][0]["text"]
                token_count = response.get("usage", {}).get("total_tokens", 0)
                
                predictions.append(output)
                latencies.append(time.time() - start_time)
                token_usages.append(token_count)
                
            except Exception as e:
                self.logger.error(f"Error evaluating test case {i}: {str(e)}")
                return result
        
        # 计算各项指标
        for metric in self.metrics:
            if metric in self.metric_functions:
                metric_value = self.metric_functions[metric](
                    predictions=predictions,
                    references=reference_outputs,
                    latencies=latencies,
                    token_usages=token_usages
                )
                setattr(result, metric, metric_value)
        
        return result
    
    async def async_evaluate(self, 
                           prompt: str, 
                           test_cases: List[Dict[str, Any]],
                           reference_outputs: Optional[List[str]] = None,
                           max_concurrent: int = 5) -> EvaluationResult:
        """异步评估提示性能，提高评估速度"""
        # 实现异步版本的评估逻辑
        # [此处省略实现代码，遵循与同步版本类似的逻辑，但使用异步模型调用]
        pass
    
    def _evaluate_accuracy(self, predictions: List[str], references: List[str],** kwargs) -> float:
        """计算准确性：完全匹配的预测比例"""
        if not predictions or not references:
            return 0.0
        correct = sum(1 for p, r in zip(predictions, references) if p.strip() == r.strip())
        return correct / len(predictions)
    
    def _evaluate_f1(self, predictions: List[str], references: List[str], **kwargs) -> float:
        """计算F1分数：基于词级别重叠"""
        # 简化实现：实际应用中应使用更复杂的文本相似度计算
        if not predictions or not references:
            return 0.0
        scores = []
        for p, r in zip(predictions, references):
            p_tokens = set(p.strip().lower().split())
            r_tokens = set(r.strip().lower().split())
            if not p_tokens and not r_tokens:
                scores.append(1.0)
                continue
            precision = len(p_tokens & r_tokens) / len(p_tokens) if p_tokens else 0
            recall = len(p_tokens & r_tokens) / len(r_tokens) if r_tokens else 0
            if precision + recall == 0:
                scores.append(0.0)
            else:
                scores.append(2 * (precision * recall) / (precision + recall))
        return sum(scores) / len(scores)
    
    def _evaluate_latency(self, latencies: List[float], **kwargs) -> float:
        """计算平均延迟"""
        return sum(latencies) / len(latencies) if latencies else 0.0
    
    def _evaluate_token_usage(self, token_usages: List[int],** kwargs) -> int:
        """计算平均token使用量"""
        return sum(token_usages) // len(token_usages) if token_usages else 0
    
    def _evaluate_cost(self, token_usages: List[int], **kwargs) -> float:
        """计算平均成本"""
        avg_tokens = self._evaluate_token_usage(token_usages)
        return avg_tokens * self.cost_per_token
    
    def _evaluate_robustness(self, predictions: List[str], references: List[str],** kwargs) -> float:
        """评估预测的稳健性：结果一致性的标准差"""
        if len(predictions) < 2:
            return 0.0
        # 计算预测之间的相似度矩阵
        similarities = []
        for i in range(len(predictions)):
            for j in range(i+1, len(predictions)):
                p1_tokens = set(predictions[i].strip().lower().split())
                p2_tokens = set(predictions[j].strip().lower().split())
                if not p1_tokens and not p2_tokens:
                    sim = 1.0
                else:
                    sim = len(p1_tokens & p2_tokens) / len(p1_tokens | p2_tokens)
                similarities.append(sim)
        # 稳健性分数 = 1 - 相似度标准差
        return 1.0 - np.std(similarities) if similarities else 0.0
    
    def compare_prompts(self, 
                       prompts: List[str], 
                       prompt_names: List[str],
                       test_cases: List[Dict[str, Any]],
                       reference_outputs: Optional[List[str]] = None) -> pd.DataFrame:
        """比较多个提示的性能，返回DataFrame结果"""
        results = []
        for name, prompt in zip(prompt_names, prompts):
            eval_result = self.evaluate(prompt, test_cases, reference_outputs)
            result_dict = eval_result.to_dict()
            result_dict["prompt_name"] = name
            results.append(result_dict)
        return pd.DataFrame(results)
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/WKw1GjumiA6Hgc72QqoeCv8LNxPB.png)

    import time
    import logging
    from typing import Any, Dict, Optional, Callable, List
    
    class RobustAPIClient:
    """带重试和故障转移机制的API客户端"""
    
    def __init__(self, 
                 primary_client: Any,
                 backup_clients: Optional[List[Any]] = None,
                 max_retries: int = default_max_retries,
                 backoff_factor: float = 0.3,
                 timeout: int = 30,
                 cost_threshold: float = None,
                 logger: Optional[logging.Logger] = None):
        self.primary_client = primary_client
        self.backup_clients = backup_clients or []
        self.max_retries = max_retries
        self.backoff_factor = backoff_factor
        self.timeout = timeout
        self.cost_threshold = cost_threshold
        self.logger = logger or logging.getLogger(__name__)
        self.call_stats = {
            "success_count": 0,
            "failure_count": 0,
            "retry_count": 0,
            "failover_count": 0,
            "total_cost": 0.0
        }
    
    def _calculate_backoff(self, retry_number: int) -> float:
        """计算指数退避延迟"""
        return self.backoff_factor * (2 **(retry_number - 1))
    
    def _is_retryable_error(self, error: Exception) -> bool:
        """判断错误是否可重试"""
        retryable_errors = [
            ConnectionError,
            TimeoutError,
            # 添加特定API的可重试错误类型
        ]
        for err_type in retryable_errors:
            if isinstance(error, err_type):
                return True
        # 检查错误消息中的重试提示
        error_msg = str(error).lower()
        retryable_messages = ["timeout", "rate limit", "temporary", "try again"]
        return any(msg in error_msg for msg in retryable_messages)
    
    def _log_and_count_failure(self, error: Exception, context: str):
        """记录失败并更新统计信息"""
        self.logger.warning(f"API call failed {context}: {str(error)}")
        self.call_stats["failure_count"] += 1
    
    def generate(self, 
                 prompt: str,
                 params: Optional[Dict[str, Any]] = None) -> Optional[Dict[str, Any]]:
        """
        带重试和故障转移的生成调用
        
        Args:
            prompt: 输入提示
            params: 模型参数
            
        Returns:
            API响应，或None（所有尝试失败时）
        """
        params = params or {}
        clients = [self.primary_client] + self.backup_clients
        
        for client_idx, client in enumerate(clients):
            for retry in range(self.max_retries + 1):
                try:
                    # 在成本阈值内才执行
                    if self.cost_threshold and self.call_stats["total_cost"] >= self.cost_threshold:
                        self.logger.error("Cost threshold exceeded, aborting API call")
                        return None
                    
                    # 执行API调用
                    start_time = time.time()
                    response = client.generate(prompt,** params)
                    duration = time.time() - start_time
                    
                    # 更新成功统计
                    self.call_stats["success_count"] += 1
                    
                    # 记录成本（如果可用）
                    token_count = response.get("usage", {}).get("total_tokens", 0)
                    cost = token_count * self.cost_per_token
                    self.call_stats["total_cost"] += cost
                    
                    self.logger.debug(
                        f"API call succeeded (client {client_idx}, retry {retry}): "
                        f"{duration:.2f}s, {token_count} tokens, ${cost:.4f}"                        
                    )
                    return response
                    
                except Exception as e:
                    # 检查是否是最后一次尝试
                    is_last_retry = (retry == self.max_retries)
                    is_last_client = (client_idx == len(clients) - 1)
                    
                    if not is_last_retry and self._is_retryable_error(e):
                        # 重试机制
                        backoff_time = self._calculate_backoff(retry)
                        self.logger.warning(
                            f"API call failed, retrying in {backoff_time:.2f}s: {str(e)}"
                        )
                        self.call_stats["retry_count"] += 1
                        time.sleep(backoff_time)
                        continue
                    elif not is_last_client:
                        # 故障转移到备用客户端
                        self._log_and_count_failure(e, f"switching to backup client {client_idx + 1}")
                        self.call_stats["failover_count"] += 1
                        break
                    else:
                        # 所有尝试失败
                        self._log_and_count_failure(e, "all attempts exhausted")
                        return None
        
        # 所有客户端和重试都失败
        self.logger.error("All API clients failed")
        return None
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/kfNdagbSyz0rlBFJ7GVjWYucPMD9.png)