About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – AP/AR Process Optimization
- Language – English
- Category – Invoice Automation
- Prompt Title – AI Prompt for Automating Invoice Processing and Payment Scheduling
Prompt Details
This prompt is designed to be adaptable across various AI platforms for automating invoice processing and payment scheduling within the Accounts Payable/Accounts Receivable (AP/AR) process optimization niche. It focuses on extracting key information, validating data, and scheduling payments based on predefined rules and user preferences.
**Prompt Structure:**
“`
## Invoice Processing and Payment Scheduling Automation
**1. Input Data (Dynamic):**
“`json
{
“invoice_data”: {
“invoice_number”: “[Invoice Number]”,
“invoice_date”: “[Invoice Date (YYYY-MM-DD)]”,
“due_date”: “[Due Date (YYYY-MM-DD)]”,
“vendor_name”: “[Vendor Name]”,
“total_amount”: “[Total Amount]”,
“line_items”: [
{
“description”: “[Item Description]”,
“quantity”: “[Quantity]”,
“unit_price”: “[Unit Price]”,
“line_total”: “[Line Total]”
},
// … more line items
],
“payment_terms”: “[Payment Terms (e.g., Net 30, 2/10 Net 30)]”,
“purchase_order_number”: “[Purchase Order Number (Optional)]”,
“invoice_file”: “[Path to Invoice File (e.g., URL, local file path)]” // Can be PDF, image, or text
},
“user_preferences”: {
“approval_workflow”: “[Approval Workflow (e.g., automatic, single-level, multi-level)]”,
“payment_method”: “[Preferred Payment Method (e.g., ACH, check, wire transfer)]”,
“early_payment_discount_threshold”: “[Percentage discount threshold for early payment (e.g., 2)]”,
“default_payment_account”: “[Default Bank Account for Payments]”,
“payment_schedule_generation”: “[Automatic/Manual]”, // Determine whether to automatically generate payment schedule
“preferred_payment_date_logic”: “[e.g., ‘On due date’, ‘X days before due date’, ‘Specific day of the month’]”,
“days_before_due_date”: “[Number of days before due date, if applicable]”,
“specific_day_of_month”: “[Day of the month for payment, if applicable]”
}
}
“`
**2. Instructions (Dynamic):**
* **Extract Data:** Extract all relevant information from the `invoice_data`, including invoice number, date, due date, vendor name, total amount, line items, payment terms, purchase order number (if present), and any other pertinent details from the invoice file itself. Use OCR if the invoice file is an image or PDF. Perform data validation to ensure accuracy. Flag any discrepancies or missing information.
* **Validate Data:** Cross-reference extracted invoice data with existing vendor records (if available). Verify vendor name, payment terms, and purchase order number against existing databases or systems.
* **Apply Payment Terms:** Calculate the payment due date based on the extracted payment terms. Factor in any early payment discounts if applicable based on `user_preferences`.
* **Generate Payment Schedule:** If `payment_schedule_generation` is “Automatic”, generate a payment schedule based on the user’s preferred payment logic defined in `preferred_payment_date_logic`, `days_before_due_date`, and `specific_day_of_month`. If “Manual”, simply present the calculated due date and discount date (if applicable).
* **Approval Workflow (Optional):** If an approval workflow is defined in `user_preferences`, indicate the necessary steps and individuals involved in the approval process. If `approval_workflow` is “automatic”, assume approval is granted.
**3. Output (Structured):**
Return the extracted and validated invoice data in a structured JSON format. Include calculated fields such as the payment due date, discount date (if applicable), and proposed payment date. Indicate any data discrepancies or validation errors. If a payment schedule is generated, include it within the output.
“`json
{
“extracted_data”: {
// … extracted invoice data
},
“validated_data”: {
// … validated data and any discrepancies
},
“payment_information”: {
“due_date”: “[Calculated Due Date]”,
“discount_date”: “[Calculated Discount Date (if applicable)]”,
“proposed_payment_date”: “[Proposed Payment Date based on user preferences]”,
“payment_method”: “[Preferred Payment Method]”,
“payment_account”: “[Default Payment Account]”
},
“payment_schedule”: [ // Only if generated
{
“payment_date”: “[Scheduled Payment Date]”,
“amount”: “[Payment Amount]”
}
],
“approval_status”: “[Approval Status (e.g., Approved, Pending, Rejected)]”,
“errors”: [ // List any errors encountered during processing
{“error_type”: “[Type of error]”, “message”: “[Error message]”}
]
}
“`
**Example Usage Scenarios:**
* **Scenario 1: Automatic Invoice Processing and Payment Scheduling:** Input invoice data and user preferences with “automatic” settings for approval and payment scheduling. The AI should extract, validate, and schedule the payment automatically.
* **Scenario 2: Invoice Data Extraction and Validation:** Input invoice data without user preferences. The AI should focus on extracting and validating the invoice information.
* **Scenario 3: Manual Payment Scheduling:** Input invoice data and user preferences with “manual” payment scheduling. The AI should calculate the due date and discount date but leave the final payment scheduling to the user.
This dynamic prompt allows for customization based on specific requirements and integrates seamlessly with existing AP/AR workflows. By structuring the input and output as JSON, integration with other systems becomes straightforward. This promotes efficient and accurate invoice processing, leading to improved cash flow management and reduced manual effort.