Prompt Engineering & LLM Optimization for Developers · 课时

数据提取与摘要生成

掌握从非结构化文本中提取特定信息，并使用 LLM 为大型文档生成简洁摘要的技术。

数据提取与摘要生成 是 CoddyKit 上的免费 Prompt Engineering & LLM Optimization for Developers 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 Prompt Engineering & LLM Optimization for Developers 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Prompt Engineering & LLM Optimization for Developers 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

LLMs for Data Handling

Large Language Models (LLMs) are incredibly powerful for processing text. They can transform unstructured information into formats that are easy for computers to understand and use.

This lesson explores two key applications: data extraction (pulling specific info) and summarization (condensing long texts).

What is Data Extraction?

Data extraction is the process of identifying and pulling specific pieces of information from a larger body of text. Think of it like finding a needle in a haystack, but the LLM helps you do it automatically.

Examples: Extracting names, dates, addresses, product IDs, or sentiment from customer reviews.
It transforms free-form text into structured data you can analyze.

Prompting for Extraction

To extract data effectively, your prompt needs to be clear and precise:

Specify Fields: Clearly list what information you need.
Define Format: Tell the LLM how to output the data (e.g., a list, key-value pairs, JSON).
Handle Missing Info: Instruct what to do if a piece of information isn't found (e.g., return 'N/A').

Code Demo: Basic Extraction

Let's see a simple Python example to extract a customer name and their purchased item from an order note. We'll use a mock function to represent the LLM API call.

def mock_llm_api_call(prompt):
  # Simulate LLM response for extraction
  if "customer name" in prompt and "item purchased" in prompt:
    return "Customer: Alice Smith, Item: Laptop"
  return "Error: Could not extract."

order_note = "Order for Alice Smith, she bought a new Laptop last week."

prompt = f"""Extract the customer name and item purchased from the following text.
Text: {order_note}
Format: Customer: [name], Item: [item]"""

extracted_data = mock_llm_api_call(prompt)
print(extracted_data)

Structured Output (JSON)

For more complex extractions, especially when dealing with multiple fields or nested data, requesting output in a structured format like JSON is ideal.

JSON (JavaScript Object Notation) is a human-readable format that machines can easily parse, making integration with other applications seamless.

Code Demo: JSON Extraction

This example shows how to ask an LLM to return extracted information as a JSON object. Notice how specific the instruction is about the output format.

import json

def mock_llm_api_call(prompt):
  # Simulate LLM response for JSON extraction
  if "customer_name" in prompt and "product" in prompt:
    return '{"customer_name": "Bob Johnson", "product": "Smartphone", "quantity": 1}'
  return '{}'

review_text = "Bob Johnson ordered a new Smartphone, he loves it!"

prompt = f"""Extract the customer name, product, and quantity from the following text.
Return the output as a JSON object with keys: customer_name, product, quantity.
If quantity is not specified, default to 1.
Text: {review_text}"""

json_string = mock_llm_api_call(prompt)
parsed_data = json.loads(json_string)
print(f"Customer: {parsed_data['customer_name']}")
print(f"Product: {parsed_data['product']}")

What is Summarization?

Summarization is the process of condensing a longer piece of text into a shorter version, while retaining its core meaning and important information.

LLMs can perform two main types:

Extractive: Selecting key sentences directly from the original text.
Abstractive: Generating new sentences that capture the essence of the original text.

Prompting for Summarization

Effective summarization prompts guide the LLM on:

Desired Length: "Summarize in 3 sentences," "Provide a one-paragraph summary."
Focus: "Focus on the main arguments," "Highlight the key findings."
Audience/Tone: "Summarize for a technical audience," "Use a simple, friendly tone."

Code Demo: Document Summarization

Here's how you might summarize a longer article to get a concise overview. We'll ask for a summary focusing on key takeaways.

def mock_llm_api_call(prompt):
  # Simulate LLM response for summarization
  if "summarize" in prompt and "key takeaways" in prompt:
    return "The report highlights the importance of renewable energy and sustainable practices for future economic growth, emphasizing global collaboration."
  return "Could not summarize."

article = (
  "A new report released today details the critical need for global investment "
  "in renewable energy sources such as solar and wind power. It emphasizes "
  "that sustainable practices are not only environmentally beneficial but also "
  "crucial for long-term economic stability and job creation. The report "
  "calls for international cooperation to accelerate the transition away from "
  "fossil fuels and mitigate climate change impacts."
)

prompt = f"""Summarize the following article, focusing on the key takeaways, in no more than two sentences.
Article: {article}"""

summary = mock_llm_api_call(prompt)
print(summary)

Quick Check: Extraction & Summarization

You have a customer feedback form with the following text:

"The new feature is great! However, the login process is confusing and needs improvement. Customer ID: CUST-XYZ-001."

Which prompt is best for extracting the 'Customer ID' and a 'Summary of Feedback' into a JSON object?

Recap: Data Extraction & Summarization

In this lesson, you learned how to harness LLMs for two powerful text processing tasks:

Data Extraction: Pulling specific, structured information from unstructured text.
Summarization: Condensing long texts into shorter, coherent versions.

Mastering clear and explicit prompting, especially for structured output like JSON, is key to getting accurate and usable results from LLMs for these tasks.

常见问题解答

「数据提取与摘要生成」课时是免费的吗？

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「数据提取与摘要生成」这节课中我会学到什么？

掌握从非结构化文本中提取特定信息，并使用 LLM 为大型文档生成简洁摘要的技术。你通过在浏览器中直接运行的动手代码来练习 Prompt Engineering & LLM Optimization for Developers，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

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无需任何先前经验。CoddyKit 上的 Prompt Engineering & LLM Optimization for Developers 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

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大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

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