データ抽出と要約
LLMを使って、非構造化テキストから特定の情報を抽出し、大規模な文書を簡潔に要約する技法を身につけます。
「データ抽出と要約」はCoddyKit上の無料Prompt Engineering & LLM Optimization for Developersレッスンです。 これはレッスン3/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはPrompt Engineering & LLM Optimization for Developers学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 Prompt Engineering & LLM Optimization for Developersコースには全4レッスンが含まれています。
このレッスンの一部はまだ翻訳されておらず、英語で表示されています。
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.
よくある質問
「データ抽出と要約」レッスンは無料ですか？
はい。「データ抽出と要約」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、Prompt Engineering & LLM Optimization for Developersコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 Prompt Engineering & LLM Optimization for Developersコースには全4レッスンが含まれています。
「データ抽出と要約」で何を学びますか？
LLMを使って、非構造化テキストから特定の情報を抽出し、大規模な文書を簡潔に要約する技法を身につけます。 ブラウザで直接実行するハンズオンコードでPrompt Engineering & LLM Optimization for Developersを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。
Prompt Engineering & LLM Optimization for Developersを始めるのに経験は必要ですか？
事前経験は必要ありません。CoddyKitのPrompt Engineering & LLM Optimization for Developersは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン3/4です。
「データ抽出と要約」レッスンにはどのくらい時間がかかりますか？
ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。
このPrompt Engineering & LLM Optimization for Developersレッスンでコードを書いて実行できますか？
はい。すべてのPrompt Engineering & LLM Optimization for Developersレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。