Prompt LLMs to Extract Data from Documents

🚀 Introduction: From Unstructured Docs to Structured Data

Businesses handle huge volumes of contracts, invoices, reports, resumes, and PDFs. Manually extracting data is slow and error-prone.

With prompt engineering, LLMs can:

• Read unstructured documents

• Extract specific fields (e.g., invoice number, date, total)

• Convert results into structured formats like JSON or CSV

• Support workflows in finance, legal, and healthcare

📌 Prompting Techniques for Document Data Extraction

1. Field Extraction Prompts

Prompt
"Extract the following fields from this invoice: Invoice Number, Date, Customer Name, and Total Amount."

👉 Output

{ "Invoice Number": "INV-10293", "Date": "2025-08-12", "Customer Name": "ABC Corporation", "Total Amount": "$5,450.00" }

2. Table Extraction

Prompt
"Extract the product list table from this PDF and return it as JSON with fields: Item, Quantity, Price, Total."

👉 Output

[ {"Item": "Laptop", "Quantity": 2, "Price": 1200, "Total": 2400}, {"Item": "Mouse", "Quantity": 5, "Price": 25, "Total": 125} ]

3. Summarized Data Extraction

Prompt
"Summarize the key obligations from this legal contract in bullet points."

👉 Output

• Party A will deliver goods within 30 days.

• Party B will make payment within 45 days.

• Warranty coverage lasts 1 year.

👉 Related: AI in Legal Industry 🔗

4. Multi-Field Document Parsing

Prompt
"From this resume, extract Candidate Name, Contact Info, Skills, Education, and Work Experience in JSON format."

👉 Output

{ "Name": "Jane Doe", "Contact": "[email protected]", "Skills": ["Python", "Data Analysis", "SQL"], "Education": "M.Sc. Computer Science", "Work Experience": [ {"Company": "TechCorp", "Role": "Data Analyst", "Years": 3} ] }

5. Hybrid Extraction (Text + Structured)

Prompt
"Extract customer complaints from this feedback report and classify them as 'Billing', 'Product Quality', or 'Support'."

👉 Output

[ {"Complaint": "Late invoice delivery", "Category": "Billing"}, {"Complaint": "Laptop battery died in 2 weeks", "Category": "Product Quality"} ]

📊 Prompt Templates for Data Extraction

Use Case	Example Prompt
Invoice Parsing	“Extract invoice number, date, customer name, and total from this PDF.”
Contracts	“Summarize obligations and deadlines from this contract.”
Healthcare	“Extract patient name, age, diagnosis, and prescriptions from this medical record.”
Resumes	“Extract candidate details and return in JSON format.”
Financial Reports	“Pull quarterly revenue, expenses, and net profit into a CSV-ready format.”

✅ Benefits

• Reduces manual data entry

• Works with unstructured documents

• Flexible for multiple industries

• Saves time for finance, HR, legal, and healthcare teams

⚠️ Challenges

• Accuracy depends on document clarity (scanned vs. digital)

• May struggle with complex formatting (tables, nested sections)

• Requires post-validation for compliance-critical industries

📚 Learn AI for Document Automation

AI-driven document parsing is a game-changer for enterprises.

🚀 Learn with C# Corner’s Learn AI Platform

At LearnAI.CSharpCorner.com, you’ll explore:

• ✅ Crafting extraction prompts for invoices, contracts, and resumes

• ✅ Automating data pipelines with LLMs

• ✅ Building AI-powered business workflows

• ✅ Real-world enterprise case studies

👉 Start Learning Prompt Engineering for Data Extraction

🧠 Final Thoughts

Prompting LLMs for document extraction allows businesses to:

• Save time

• Reduce errors

• Automate repetitive workflows

The best results come from structured, specific prompts and output formats like JSON or CSV.

This is where AI meets RPA (Robotic Process Automation) — turning unstructured data into business intelligence.