AI Data Extraction with Dynamic Prompts and Airtable - n8n Workflow

Let's face it: manually copying information from PDF documents into spreadsheets is tedious, time-consuming, and soul-crushing. Whether you're processing invoices, extracting data from resumes, or pulling information from contracts, the copy-paste routine eats up hours that could be spent on meaningful work.

What if your database could intelligently read PDFs and populate itself automatically? This n8n workflow does exactly that. It combines Airtable webhooks, AI-powered data extraction, and dynamic prompts to transform how you handle document processing. Upload a PDF to Airtable, and watch as artificial intelligence reads it, extracts the exact information you need, and updates your records without any manual intervention.

Prerequisites: What You'll Need to Get Started

Before diving into the workflow setup, make sure you have access to these essential services:

• n8n instance: Either cloud-hosted or self-hosted version of n8n
• Airtable account: With a base containing a table for your data
• Airtable Personal Access Token: For API authentication
• OpenAI API key: To power the AI extraction capabilities
• PDF files: Documents you want to extract data from

Key Components: The Building Blocks

This workflow leverages several powerful n8n nodes working in harmony:

• Webhook node: Receives real-time notifications from Airtable
• Airtable nodes: Retrieves schema, fetches records, and updates data
• HTTP Request nodes: Downloads PDF files and manages webhook payloads
• Extract from File node: Converts PDF content into readable text
• OpenAI Chat Model: Powers intelligent data extraction
• Code nodes: Handles custom logic for parsing and filtering
• Split in Batches nodes: Processes multiple items efficiently

How It Works: Building Your Intelligent Extraction System

Step 1: Setting Up Airtable Webhooks

The workflow begins with creating two specialized Airtable webhooks that monitor your base for changes. The first webhook watches for updates to records when files are uploaded. The second monitors when new fields are created or existing ones are modified.

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These webhooks act as the eyes and ears of your automation. When you upload a PDF to an Airtable attachment field or add a new column with extraction instructions, the webhook immediately notifies n8n to spring into action. This real-time trigger means zero delay between uploading a document and getting your extracted data.

Step 2: Retrieving Table Schema and Event Routing

Once triggered, the workflow fetches your complete Airtable schema. This is where the magic of dynamic prompts begins. The system reads field descriptions in your Airtable base, which serve as extraction instructions for the AI.

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For example, if you have a field named "Full Name" with the description "Extract the full name of the person from the document," the AI knows exactly what to look for. The Event Type switch node then routes the workflow based on whether a row was updated, a field was created, or a field was modified. This intelligent routing ensures the right extraction logic runs at the right time.

Step 3: Processing Updates to Existing Records

When a file is uploaded to an existing row, the workflow retrieves that specific record and checks which fields still need data. The system intelligently identifies empty fields that have extraction instructions in their descriptions.

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It then downloads the PDF, extracts all text content, and loops through each empty field. For every field requiring data, the AI receives a custom prompt containing the PDF text, the extraction instruction from the field description, and the expected output format. The AI processes this information and returns precisely formatted data that gets written back to Airtable.

Step 4: Handling New or Modified Fields

The workflow handles a different scenario when you add new fields or update field descriptions. Instead of processing just one record, it searches for all records in your table that have file attachments.

For each record with a PDF, the system downloads the file, extracts the text, and generates values for the newly created or modified field. This batch processing capability means you can add a new extraction field to your base and instantly populate it across hundreds of existing records without touching each one manually.

Step 5: AI-Powered Extraction with Context

The heart of this workflow lives in the Generate Field Value nodes. These leverage OpenAI's language models with carefully crafted prompts that include the full PDF text wrapped in XML tags for clarity, the specific data to extract pulled from field descriptions, and the expected output format based on field types.

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The AI responds concisely, keeping answers short and returning "n/a" with a reason when data cannot be found. This prevents hallucination and ensures you know when information is genuinely missing from a document rather than getting fabricated data.

Step 6: Updating Records and Continuing the Loop

After extraction, the workflow updates Airtable records with the new data. The Split in Batches nodes ensure that if you have multiple records to process, each one is handled sequentially. This prevents overwhelming the AI API with too many simultaneous requests and ensures reliable processing even with large document batches.

The workflow includes reference nodes that maintain context between different processing steps, ensuring that extracted data gets written to the correct record and field every time.

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Real-World Applications: Where This Workflow Shines

This intelligent extraction system transforms numerous business processes:

• Resume screening: Automatically extract candidate names, contact information, education, and work experience from application PDFs
• Invoice processing: Pull vendor names, amounts, dates, and line items from invoices into accounting systems
• Contract management: Extract key terms, dates, parties, and obligations from legal documents
• Research data collection: Gather specific data points from academic papers or reports
• Form digitization: Convert information from scanned forms into structured database records

Why This Approach Beats Manual Processing

The benefits extend beyond just saving time. This workflow provides consistency that human data entry cannot match. The AI extracts data using the same criteria every time, eliminating the variability that comes from different people interpreting extraction instructions differently.

You also gain immediate scalability. Processing ten documents takes roughly the same effort as processing one thousand. The workflow handles batch operations seamlessly, making it practical for both small teams and enterprise-level document processing needs.

Perhaps most importantly, this system is self-documenting. Your Airtable field descriptions serve as both extraction instructions for the AI and documentation for your team. Anyone looking at your base immediately understands what data each field should contain and where it comes from.

Transform Your Document Workflow Today

This n8n workflow represents a fundamental shift in how you can handle document processing. By combining real-time webhooks, intelligent field mapping, and AI-powered extraction, you create a system that feels almost magical in its ability to understand and process documents automatically.

The setup requires some initial configuration, but once running, it eliminates countless hours of manual data entry while providing more consistent, reliable results. Whether you're processing a handful of documents weekly or hundreds daily, this workflow scales to meet your needs without breaking a sweat.

Start by setting up a simple test base with just a few fields. Upload a sample PDF and watch as your Airtable populates itself. Once you see it in action, you'll find countless ways to apply this pattern across your organization, transforming tedious data entry into automated intelligence.