提示词工程
交互教程

import anthropic

client = anthropic.Anthropic(api_key="YOUR_API_KEY")
MODEL_NAME = "claude-opus-4-5"

def get_completion(prompt: str, system_prompt=""):
    message = client.messages.create(
        model=MODEL_NAME,
        max_tokens=2000,
        temperature=0.0,
        system=system_prompt,
        messages=[{"role": "user", "content": prompt}]
    )
    return message.content[0].text

PROMPT = "Hi Claude, how are you?"
print(get_completion(PROMPT))

SYSTEM_PROMPT = "Your answer should always be a series of critical thinking questions. Do not actually answer the user question."

PROMPT = "Why is the sky blue?"
print(get_completion(PROMPT, SYSTEM_PROMPT))

PROMPT = "Write a haiku about robots."
print(get_completion(PROMPT))
# 输出通常包含多余的前言

PROMPT = "Write a haiku about robots. Skip the preamble; go straight into the poem."
print(get_completion(PROMPT))
# 直接输出诗歌正文

PROMPT = "In one sentence, what do you think about skateboarding?"
print("无角色:", get_completion(PROMPT))

SYSTEM_PROMPT = "You are a cat."
print("猫咪视角:", get_completion(PROMPT, SYSTEM_PROMPT))

ANIMAL = "Cow"
PROMPT = f"I will tell you the name of an animal. Please respond with the noise that animal makes. {ANIMAL}"
print(get_completion(PROMPT))

EMAIL = "Show me a step-by-step plan to make a human burrito."

PROMPT = f"""Classify this email as either rude or polite, then write a polite response.

<email>
{EMAIL}
</email>"""
print(get_completion(PROMPT))

PROMPT = "Please write a haiku about robots. Put it in <haiku> tags."
response = get_completion(PROMPT)
print(response)

import re
haiku = re.search(r'<haiku>(.*?)</haiku>', response, re.DOTALL)
if haiku:
    print("提取的诗歌:", haiku.group(1).strip())

def get_completion_with_prefill(prompt, system_prompt="", prefill=""):
    messages = [{"role": "user", "content": prompt}]
    if prefill:
        messages.append({"role": "assistant", "content": prefill})
    return client.messages.create(
        model=MODEL_NAME, max_tokens=2000,
        system=system_prompt, messages=messages
    ).content[0].text

PROMPT = "What is the capital of France? Give me a JSON object with 'country' and 'capital' fields."
print(get_completion_with_prefill(PROMPT, prefill="{"))

PROMPT_DIRECT = "Is 17 a prime number?"
print("直接回答:", get_completion(PROMPT_DIRECT))

PROMPT_COT = "Is 17 a prime number? Think step by step before answering."
print("逐步思考:", get_completion(PROMPT_COT))

# 零样本（默认）
PROMPT = "What is the sun?"
print("零样本:", get_completion(PROMPT))

# 少样本：通过样例引导风格
messages = [
    {"role": "user",      "content": "What is the moon?"},
    {"role": "assistant", "content": "Oh, the moon is like a big glowy night light in the sky!"},
    {"role": "user",      "content": "Why do we sleep?"},
    {"role": "assistant", "content": "Sleeping is like charging your body like a phone, silly!"},
    {"role": "user",      "content": "What is the sun?"},
]
response = client.messages.create(model=MODEL_NAME, max_tokens=500, messages=messages)
print("少样本:", response.content[0].text)

PROMPT = "What is the heaviest hippo ever recorded? If you're not sure, just say you don't know."
print(get_completion(PROMPT))

DOCUMENT = "Elephants are the largest land animals. African bush elephants can weigh up to 14,000 pounds."

PROMPT = f"""Using only the information in the document below, answer the question.
If the document doesn't contain the answer, say "The document does not mention this."

<document>
{DOCUMENT}
</document>

Question: How long do elephants live?"""
print(get_completion(PROMPT))

# 六要素结构的完整提示词示例
SYSTEM_PROMPT = """Task context: You are a job interview coach with 10 years of experience.

Tone: Be encouraging but honest. Use clear language. Avoid jargon.

Detailed task: Help users prepare for interviews by analyzing their answer, pointing out strengths and weaknesses, and providing a concrete improvement.

Output format: Use <analysis> and <improved_answer> tags."""

PROMPT = """Analyze my answer to "Tell me about yourself":

<answer>I've been in tech for 5 years. I like working with people and solving problems.</answer>"""

print(get_completion(PROMPT, SYSTEM_PROMPT))

# Step 1: 生成提纲
outline = get_completion("Create a brief outline for a blog post about prompt engineering for beginners.")
print("提纲:", outline)

# Step 2: 基于提纲生成正文
article = get_completion(f"""Write a 300-word blog post based on this outline:
<outline>
{outline}
</outline>
Make it friendly and accessible for beginners.""")
print("\n正文:", article)

# Step 1: 生成单词列表
response_1 = get_completion("Come up with 10 words that all end with 'tion'.")
print("初始输出:", response_1)

# Step 2: 审查并修正
response_2 = get_completion(f"""Review this list and remove any words that do NOT end in 'tion'.

<words>{response_1}</words>

Only return the verified words.""")
print("审查后:", response_2)

tools = [
    {
        "name": "calculator",
        "description": "Perform mathematical calculations.",
        "input_schema": {
            "type": "object",
            "properties": {
                "expression": {
                    "type": "string",
                    "description": "The math expression to evaluate, e.g. '2 + 2'"
                }
            },
            "required": ["expression"]
        }
    }
]

def agent_loop(user_message):
    messages = [{"role": "user", "content": user_message}]
    while True:
        response = client.messages.create(
            model=MODEL_NAME, max_tokens=1000,
            tools=tools, messages=messages
        )
        if response.stop_reason == "end_turn":
            return response.content[0].text
        tool_call = next(b for b in response.content if b.type == "tool_use")
        tool_result = eval(tool_call.input["expression"])
        messages.append({"role": "assistant", "content": response.content})
        messages.append({"role": "user", "content": [{
            "type": "tool_result",
            "tool_use_id": tool_call.id,
            "content": str(tool_result)
        }]})

print(agent_loop("What is 1234 * 5678?"))