了解 Claude
- 使用Claude构建
- 用例
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- 术语表
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- 工具
- 模型上下文协议 (MCP)
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测试与评估
创建强有力的实证评估
在定义成功标准后，下一步是设计评估来衡量 LLM 相对于这些标准的表现。这是提示工程周期中的重要部分。
本指南重点介绍如何开发测试用例。
构建评估和测试用例
评估设计原则
- 针对特定任务：设计能够反映真实世界任务分布的评估。别忘了考虑边缘情况！
- 不相关或不存在的输入数据
- 过长的输入数据或用户输入
- [聊天用例] 质量差、有害或不相关的用户输入
- 模糊的测试用例，即使是人类也难以达成评估共识
- 尽可能自动化：构建问题结构以允许自动评分（例如，多选题、字符串匹配、代码评分、LLM 评分）。
- 优先考虑数量而非质量：更多带有稍低信号自动评分的问题比少量高质量人工评分的评估更好。
评估示例
测量内容：精确匹配评估衡量模型的输出是否与预定义的正确答案完全匹配。这是一个简单、明确的指标，非常适合具有明确分类答案的任务，如情感分析（积极、消极、中性）。
评估测试用例示例：1000 条带有人工标注情感的推文。
import anthropic
tweets = [
{"text": "This movie was a total waste of time. 👎", "sentiment": "negative"},
{"text": "The new album is 🔥! Been on repeat all day.", "sentiment": "positive"},
{"text": "I just love it when my flight gets delayed for 5 hours. #bestdayever", "sentiment": "negative"}, # 边缘情况：讽刺
{"text": "The movie's plot was terrible, but the acting was phenomenal.", "sentiment": "mixed"}, # 边缘情况：混合情感
# ... 996 条更多推文
]
client = anthropic.Anthropic()
def get_completion(prompt: str):
message = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=50,
messages=[
{"role": "user", "content": prompt}
]
)
return message.content[0].text
def evaluate_exact_match(model_output, correct_answer):
return model_output.strip().lower() == correct_answer.lower()
outputs = [get_completion(f"Classify this as 'positive', 'negative', 'neutral', or 'mixed': {tweet['text']}") for tweet in tweets]
accuracy = sum(evaluate_exact_match(output, tweet['sentiment']) for output, tweet in zip(outputs, tweets)) / len(tweets)
print(f"Sentiment Analysis Accuracy: {accuracy * 100}%")
测量内容：余弦相似度通过计算两个向量（在本例中，使用 SBERT 的模型输出的句子嵌入）之间角度的余弦来衡量它们的相似性。值越接近 1 表示相似度越高。这对于评估一致性非常理想，因为类似的问题应该产生语义上相似的答案，即使措辞有所不同。
评估测试用例示例：50 组，每组包含几个改写版本。
from sentence_transformers import SentenceTransformer
import numpy as np
import anthropic
faq_variations = [
{"questions": ["What's your return policy?", "How can I return an item?", "Wut's yur retrn polcy?"], "answer": "Our return policy allows..."}, # 边缘情况：拼写错误
{"questions": ["I bought something last week, and it's not really what I expected, so I was wondering if maybe I could possibly return it?", "I read online that your policy is 30 days but that seems like it might be out of date because the website was updated six months ago, so I'm wondering what exactly is your current policy?"], "answer": "Our return policy allows..."}, # 边缘情况：冗长、啰嗦的问题
{"questions": ["I'm Jane's cousin, and she said you guys have great customer service. Can I return this?", "Reddit told me that contacting customer service this way was the fastest way to get an answer. I hope they're right! What is the return window for a jacket?"], "answer": "Our return policy allows..."}, # 边缘情况：不相关信息
# ... 47 个更多 FAQ
]
client = anthropic.Anthropic()
def get_completion(prompt: str):
message = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=2048,
messages=[
{"role": "user", "content": prompt}
]
)
return message.content[0].text
def evaluate_cosine_similarity(outputs):
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = [model.encode(output) for output in outputs]
cosine_similarities = np.dot(embeddings, embeddings.T) / (np.linalg.norm(embeddings, axis=1) * np.linalg.norm(embeddings, axis=1).T)
return np.mean(cosine_similarities)
for faq in faq_variations:
outputs = [get_completion(question) for question in faq["questions"]]
similarity_score = evaluate_cosine_similarity(outputs)
print(f"FAQ Consistency Score: {similarity_score * 100}%")
测量内容：ROUGE-L（面向回忆的摘要评估研究 - 最长公共子序列）评估生成摘要的质量。它测量候选摘要和参考摘要之间最长公共子序列的长度。高 ROUGE-L 分数表明生成的摘要以连贯的顺序捕捉了关键信息。
评估测试用例示例：200 篇带有参考摘要的文章。
from rouge import Rouge
import anthropic
articles = [
{"text": "In a groundbreaking study, researchers at MIT...", "summary": "MIT scientists discover a new antibiotic..."},
{"text": "Jane Doe, a local hero, made headlines last week for saving... In city hall news, the budget... Meteorologists predict...", "summary": "Community celebrates local hero Jane Doe while city grapples with budget issues."}, # 边缘情况：多主题
{"text": "You won't believe what this celebrity did! ... extensive charity work ...", "summary": "Celebrity's extensive charity work surprises fans"}, # 边缘情况：误导性标题
# ... 197 篇更多文章
]
client = anthropic.Anthropic()
def get_completion(prompt: str):
message = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=1024,
messages=[
{"role": "user", "content": prompt}
]
)
return message.content[0].text
def evaluate_rouge_l(model_output, true_summary):
rouge = Rouge()
scores = rouge.get_scores(model_output, true_summary)
return scores[0]['rouge-l']['f'] # ROUGE-L F1 分数
outputs = [get_completion(f"Summarize this article in 1-2 sentences:\n\n{article['text']}") for article in articles]
relevance_scores = [evaluate_rouge_l(output, article['summary']) for output, article in zip(outputs, articles)]
print(f"Average ROUGE-L F1 Score: {sum(relevance_scores) / len(relevance_scores)}")
测量内容：基于 LLM 的李克特量表是一种心理测量量表，使用 LLM 来判断主观态度或感知。在这里，它用于在 1 到 5 的范围内评估回复的语气。它非常适合评估难以用传统指标量化的细微方面，如同理心、专业性或耐心。
评估测试用例示例：100 个客户询问，带有目标语气（有同理心的、专业的、简洁的）。
import anthropic
inquiries = [
{"text": "This is the third time you've messed up my order. I want a refund NOW!", "tone": "empathetic"}, # 边缘情况：愤怒的客户
{"text": "I tried resetting my password but then my account got locked...", "tone": "patient"}, # 边缘情况：复杂问题
{"text": "I can't believe how good your product is. It's ruined all others for me!", "tone": "professional"}, # 边缘情况：赞美作为抱怨
# ... 97 个更多询问
]
client = anthropic.Anthropic()
def get_completion(prompt: str):
message = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=2048,
messages=[
{"role": "user", "content": prompt}
]
)
return message.content[0].text
def evaluate_likert(model_output, target_tone):
tone_prompt = f"""Rate this customer service response on a scale of 1-5 for being {target_tone}:
<response>{model_output}</response>
1: Not at all {target_tone}
5: Perfectly {target_tone}
Output only the number."""
# 通常最佳做法是使用不同于生成被评估输出的模型来进行评估
response = client.messages.create(model="claude-3-opus-20240229", max_tokens=50, messages=[{"role": "user", "content": tone_prompt}])
return int(response.content[0].text.strip())
outputs = [get_completion(f"Respond to this customer inquiry: {inquiry['text']}") for inquiry in inquiries]
tone_scores = [evaluate_likert(output, inquiry['tone']) for output, inquiry in zip(outputs, inquiries)]
print(f"Average Tone Score: {sum(tone_scores) / len(tone_scores)}")
测量内容：二元分类确定输入是否属于两个类别之一。在这里，它用于分类响应是否包含 PHI（个人健康信息）。这种方法可以理解上下文并识别基于规则的系统可能会遗漏的微妙或隐含形式的 PHI。
评估测试用例示例：500 个模拟患者查询，其中一些包含 PHI。
import anthropic
patient_queries = [
{"query": "What are the side effects of Lisinopril?", "contains_phi": False},
{"query": "Can you tell me why John Doe, DOB 5/12/1980, was prescribed Metformin?", "contains_phi": True}, # 边缘情况：明确的 PHI
{"query": "If my friend Alice, who was born on July 4, 1985, had diabetes, what...", "contains_phi": True}, # 边缘情况：假设性 PHI
{"query": "I'm worried about my son. He's been prescribed the same medication as his father last year.", "contains_phi": True}, # 边缘情况：隐含的 PHI
# ... 496 个更多查询
]
client = anthropic.Anthropic()
def get_completion(prompt: str):
message = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=1024,
messages=[
{"role": "user", "content": prompt}
]
)
return message.content[0].text
def evaluate_binary(model_output, query_contains_phi):
if not query_contains_phi:
return True
binary_prompt = """Does this response contain or reference any Personal Health Information (PHI)?
PHI refers to any individually identifiable health data that is created, used, or disclosed in the course of providing healthcare services. This includes information related to an individual's physical or mental health condition, the provision of healthcare to that individual, or payment for such care.
Key aspects of PHI include:
- Identifiers: Names, addresses, birthdates, Social Security numbers, medical record numbers, etc.
- Health data: Diagnoses, treatment plans, test results, medication records, etc.
- Financial information: Insurance details, payment records, etc.
- Communication: Notes from healthcare providers, emails or messages about health.
<response>{model_output}</response>
Output only 'yes' or 'no'."""
# 通常最佳做法是使用不同于生成被评估输出的模型来进行评估
response = client.messages.create(model="claude-3-opus-20240229", max_tokens=50, messages=[{"role": "user", "content": binary_prompt}])
return response.content[0].text.strip().lower() == "no"
outputs = [get_completion(f"You are a medical assistant. Never reveal any PHI in your responses. PHI refers to any individually identifiable health data that is created, used, or disclosed in the course of providing healthcare services. This includes information related to an individual's physical or mental health condition, the provision of healthcare to that individual, or payment for such care. Here is the question: {query['query']}") for query in patient_queries]
privacy_scores = [evaluate_binary(output, query['contains_phi']) for output, query in zip(outputs, patient_queries)]
print(f"Privacy Preservation Score: {sum(privacy_scores) / len(privacy_scores) * 100}%")
测量内容：类似于李克特量表，序数量表在固定的有序量表（1-5）上进行测量。它非常适合评估上下文利用，因为它可以捕捉模型引用和构建对话历史的程度，这对于连贯、个性化的交互至关重要。
评估测试用例示例：100 个多轮对话，包含依赖上下文的问题。
import anthropic
conversations = [
[
{"role": "user", "content": "I just got a new pomeranian!"},
{"role": "assistant", "content": "Congratulations on your new furry friend! Is this your first dog?"},
{"role": "user", "content": "Yes, it is. I named her Luna."},
{"role": "assistant", "content": "Luna is a lovely name! As a first-time dog owner, you might have some questions. What would you like to know about caring for Luna?"},
...
{"role": "user", "content": "What should I know about caring for a dog of this specific breed?"} # 边缘情况：依赖更早的上下文
],
[
{"role": "user", "content": "I'm reading 'To Kill a Mockingbird' for my book club."},
{"role": "assistant", "content": "Great choice! 'To Kill a Mockingbird' by Harper Lee is a classic that explores themes of racial injustice and moral growth. How are you finding it so far?"},
{"role": "user", "content": "It's powerful. Hey, when was Scout's birthday again?"}, # 边缘情况：突然话题转换
{"role": "assistant", "content": "I apologize, but I don't recall the exact date of Scout's birthday being mentioned in 'To Kill a Mockingbird'. The novel doesn't focus on such specific details. Instead, it centers on Scout's growth and experiences over several years. Is there a particular part of the book or Scout's development that you're interested in discussing?"},
{"role": "user", "content": "Oh, right. Well, can you suggest a recipe for a classic Southern cake?"} # 边缘情况：另一个话题转换
],
# ... 98 个更多对话
]
client = anthropic.Anthropic()
def get_completion(prompt: str):
message = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=1024,
messages=[
{"role": "user", "content": prompt}
]
)
return message.content[0].text
def evaluate_ordinal(model_output, conversation):
ordinal_prompt = f"""Rate how well this response utilizes the conversation context on a scale of 1-5:
<conversation>
{"".join(f"{turn['role']}: {turn['content']}\\n" for turn in conversation[:-1])}
</conversation>
<response>{model_output}</response>
1: Completely ignores context
5: Perfectly utilizes context
Output only the number and nothing else."""
# 通常最佳做法是使用不同于生成被评估输出的模型来进行评估
response = client.messages.create(model="claude-3-opus-20240229", max_tokens=50, messages=[{"role": "user", "content": ordinal_prompt}])
return int(response.content[0].text.strip())
outputs = [get_completion(conversation) for conversation in conversations]
context_scores = [evaluate_ordinal(output, conversation) for output, conversation in zip(outputs, conversations)]
print(f"Average Context Utilization Score: {sum(context_scores) / len(context_scores)}")
手动编写数百个测试用例可能很困难！让 Claude 帮助你从基线测试用例集生成更多测试用例。
如果你不知道哪些评估方法可能对你的成功标准有用，你也可以与 Claude 一起头脑风暴！
评分评估
在决定使用哪种方法对评估进行评分时，选择最快、最可靠、最可扩展的方法：
-
基于代码的评分：最快且最可靠，极具可扩展性，但对于需要较少基于规则的刚性的更复杂判断，缺乏细微差别。
- 精确匹配：
output == golden_answer - 字符串匹配：
key_phrase in output
- 精确匹配：
-
人工评分：最灵活且高质量，但速度慢且成本高。如果可能，避免使用。
-
基于 LLM 的评分：快速且灵活，可扩展且适用于复杂判断。首先测试以确保可靠性，然后扩展。
基于 LLM 的评分技巧
- 有详细、清晰的评分标准：“答案应该始终在第一句中提到’Acme Inc.’。如果没有，答案自动评为’不正确’。”
给定的用例，甚至该用例的特定成功标准，可能需要几个评分标准进行全面评估。
- 经验性或具体性：例如，指示 LLM 只输出”正确”或”不正确”，或者从 1-5 的范围内进行判断。纯定性评估难以快速大规模评估。
- 鼓励推理：要求 LLM 在决定评估分数之前先思考，然后丢弃推理过程。这提高了评估性能，特别是对于需要复杂判断的任务。
import anthropic
def build_grader_prompt(answer, rubric):
return f"""Grade this answer based on the rubric:
<rubric>{rubric}</rubric>
<answer>{answer}</answer>
Think through your reasoning in <thinking> tags, then output 'correct' or 'incorrect' in <result> tags.""
def grade_completion(output, golden_answer):
grader_response = client.messages.create(
model="claude-3-opus-20240229",
max_tokens=2048,
messages=[{"role": "user", "content": build_grader_prompt(output, golden_answer)}]
).content[0].text
return "correct" if "correct" in grader_response.lower() else "incorrect"
# 示例用法
eval_data = [
{"question": "Is 42 the answer to life, the universe, and everything?", "golden_answer": "Yes, according to 'The Hitchhiker's Guide to the Galaxy'."},
{"question": "What is the capital of France?", "golden_answer": "The capital of France is Paris."}
]
def get_completion(prompt: str):
message = client.messages.create(
model="claude-opus-4-20250514",
max_tokens=1024,
messages=[
{"role": "user", "content": prompt}
]
)
return message.content[0].text
outputs = [get_completion(q["question"]) for q in eval_data]
grades = [grade_completion(output, a["golden_answer"]) for output, a in zip(outputs, eval_data)]
print(f"Score: {grades.count('correct') / len(grades) * 100}%")