Data Extraction & Summarization
Master techniques for extracting specific information from unstructured text and generating concise summaries of large documents using LLMs.
Data Extraction & Summarization is a free Prompt Engineering & LLM Optimization for Developers lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Prompt Engineering & LLM Optimization for Developers learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
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.
Frequently asked questions
Is the “Data Extraction & Summarization” lesson free?
Yes — the full text of “Data Extraction & Summarization” is free to read here on the web, and the Prompt Engineering & LLM Optimization for Developers course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Prompt Engineering & LLM Optimization for Developers course, upgrade to CoddyKit PRO.
What will I learn in “Data Extraction & Summarization”?
Master techniques for extracting specific information from unstructured text and generating concise summaries of large documents using LLMs. You practise Prompt Engineering & LLM Optimization for Developers with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Prompt Engineering & LLM Optimization for Developers?
No prior experience is required. Prompt Engineering & LLM Optimization for Developers on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Data Extraction & Summarization” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Prompt Engineering & LLM Optimization for Developers lesson?
Yes. Every Prompt Engineering & LLM Optimization for Developers lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Code Generation & Refactoring
- Debugging & Test Case Generation
- Data Extraction & Summarization
- Generating SQL Queries from Natural Language