Traditionally, this has involved a combination of rule-based systems, regular expressions, and natural language processing (NLP) pipelines. But with the rise of Large Language Models (LLMs), text extraction has entered a new era—one where AI can understand context, infer structure, and adapt to messy data with minimal hand-holding.
In this blog, we’ll explore how text extraction using LLMs works, why it's better than traditional approaches, and how you can implement it in your workflows.
What is Text Extraction?
Text extraction is the process of identifying and retrieving specific information from unstructured text. For example, given an email, you might want to extract the sender, date, action items, and sentiment. From a legal contract, you might extract the names of parties, effective dates, or termination clauses.
The goal is to convert raw text into structured data that can be stored, analyzed, or passed into other systems.
Why Traditional Methods Struggle
Traditional NLP or regex-based extraction techniques rely on:
- Fixed patterns (e.g., look for “Name: ___”)
- Predefined entities (using Named Entity Recognition)
- Template-based parsing
These methods work fine for predictable formats, but they break down when:
- The language is ambiguous or inconsistent
- The formatting varies across documents
- There's no clear structure or label to guide parsing
This is where LLMs shine.
How LLMs Improve Text Extraction
Large Language Models, like GPT or open-source alternatives, are trained on massive corpora and can generalize across formats, domains, and document types. They bring the following advantages to text extraction:
1. Context Awareness
LLMs don’t just match patterns—they understand meaning. If a contract says, "This agreement shall remain valid until June 30, 2025," the model can identify June 30, 2025 as the expiry date without needing an explicit label.
2. Domain Adaptability
LLMs can extract information from legal, financial, medical, or technical documents—even if you don’t fine-tune them—because they’ve been trained on diverse datasets.
3. Zero- or Few-Shot Extraction
You can instruct an LLM to extract information using plain text prompts. No training data or complex NLP pipelines required. For example:
Extract the customer name, address, and invoice total from the text below.
4. Flexible Output Formats
LLMs can output structured JSON, CSV, or key-value pairs, making it easy to integrate with downstream systems.
Real-World Use Cases
- Invoices & Receipts: Extract vendor name, total, line items, tax, and date.
- Customer Support Logs: Identify intent, sentiment, and key issues raised.
- Legal Contracts: Pull out party names, dates, obligations, and clauses.
- Resumes & CVs: Extract experience, skills, education, and contact info.
- Emails: Capture sender, subject, tasks, dates, and tone.
How to Perform Text Extraction Using LLMs
Here’s a basic outline of how to extract structured data using an LLM:
- Prepare Input Text
Clean the raw data (if needed), such as removing HTML tags, fixing OCR errors, or merging lines.
Define a Prompt
Write a clear instruction describing what you want the model to extract. Example:
Extract the following details from the invoice:
- Invoice Number
- Vendor Name
- Date
- Total Amount
Output the result in JSON format.
- Send to LLM API
Pass the prompt and the input text to the LLM using an API (or local model if you're running one).
- Parse the Response
Most LLMs can return the response in structured form (like JSON), making it easy to validate and store.
- Validate Output
Add optional rules or checks to confirm the extracted data is complete and accurate.
Best Practices
- Keep prompts simple and explicit.
- Use examples if the structure is inconsistent.
- Limit input length or chunk long texts.
- Use post-processing to clean or normalize extracted fields.
- Log inputs and outputs for auditing and improvement.
Testing LLM-Based Extractors with Keploy
Once your LLM-based text extractor is integrated into your application, it’s essential to test it in real-world scenarios. This is where Keploy comes in.
Keploy is an open-source testing platform that automatically captures API traffic and generates test cases from real user interactions. If your backend uses LLMs to process text and return extracted data, Keploy can observe those requests and:
- Generate test cases based on actual inputs and outputs
- Mock external APIs (like LLM services)
- Help you replay test cases during CI/CD pipelines
This means you can build reliable LLM-driven applications without writing test cases manually. You’ll catch regressions early, verify accuracy, and maintain confidence even as you update prompts or models.
Final Thoughts
Text extraction using LLMs is a game-changer for any organization working with unstructured data. It simplifies development, adapts to real-world variability, and delivers highly accurate results with minimal setup. Whether you're parsing contracts, invoices, or emails, LLMs help you turn messy text into clean, usable data.
And with tools like Keploy, you can ensure your text extraction workflows remain accurate, consistent, and testable—no matter how your code or data changes.
Start building smarter, faster, and more reliable AI tools today. Use https://keploy.io/blog/community/llm-txt-generator
LLMs for extraction and Keploy for effortless testing.
Let me know if you want this optimized for a specific industry or platform!