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Reference Implementation: LLM Prompts for Date Extraction

What it is

A specialized prompt template designed for Large Language Models (LLMs) to extract structured event and date information from raw OCR (Optical Character Recognition) text. It focuses on converting unstructured human language into a precise JSON format compatible with calendar APIs.

What problem it solves

Scanned documents (receipts, school flyers, medical letters) contain critical dates that are often buried in noise. Manual entry is error-prone and tedious. This prompt automates the extraction process, handling relative dates (e.g., "next Tuesday") and implicit context that traditional regex-based scrapers miss.

Where it fits in the stack

This implementation sits in the LLM reasoning layer of the ingestion pipeline. It is typically invoked by n8n after a document has been OCR'd and before the data is passed to the HITL UI or a calendar synchronization script.

Typical use cases

  • Automated Scheduling: Extracting appointment dates from medical referral letters and adding them to a family calendar.
  • Deadline Tracking: Identifying due dates on utility bills or tax documents to trigger automated reminders.
  • School Calendar Ingestion: Processing flyers for school events (plays, holidays, parent-teacher conferences) from a scanned image.

Strengths

  • Contextual Awareness: Can resolve relative dates if provided with the "current date" context.
  • Noise Resilience: Effective at ignoring boilerplate text, headers, and footers common in OCR output.
  • Structured Output: Guarantees a JSON response for easy downstream processing.

Limitations

  • Hallucination Risk: LLMs may occasionally "invent" dates if the OCR text is highly garbled or ambiguous.
  • Token Usage: Long documents with a lot of irrelevant text can consume significant prompt tokens.
  • Relative Date Complexity: Highly complex relative dates may still confuse smaller models. Frontier models like GPT-5.5 or Claude 4.8 have significantly improved reasoning for complex temporal logic.

When to use it

  • When you need to extract dates from unstructured documents where the layout is not consistent.
  • When the ingestion pipeline includes a human-in-the-loop (HITL) step to verify the extraction.

When not to use it

  • For documents with a fixed, known layout where simple regex or positional scraping is 100% reliable and cheaper.
  • For high-volume, low-latency applications where the cost/time of LLM inference is prohibitive.

Getting started

  1. Set up an OCR engine (e.g., Tesseract or Paperless-ngx) to convert your documents to text.
  2. Configure an n8n workflow to receive the OCR text.
  3. Use the prompt template provided below in an LLM node (Ollama, OpenAI, or Anthropic).
  4. Ensure you inject the current ISO date into the prompt to resolve relative temporal references.

Prompt Template

You are a precision administrative assistant.
Analyze the provided OCR text from a document and extract any upcoming events or deadlines.

Text:
{{ocr_text}}

Current Date: {{current_date}}

Return ONLY a JSON object with the following fields:
{
  "event_name": "string",
  "start_date": "ISO8601 string",
  "end_date": "ISO8601 string or null",
  "location": "string or null",
  "reasoning": "brief explanation of why these dates were chosen"
}
If no event is found, return {"event_name": null}.

CLI examples

You can test extraction from a text file using the openai CLI tool.

# Extract dates from a text file using GPT-5.5
cat ocr_output.txt | openai api chat.completions.create \
  -m gpt-5.5-preview \
  -g system "You are a precision administrative assistant. Return JSON." \
  -g user

API examples

Integration via Python for automated pipelines using the openai library.

import openai

def extract_dates(ocr_text, current_date):
    response = openai.chat.completions.create(
        model="gpt-5.5-preview",
        messages=[
            {"role": "system", "content": "You are a precision administrative assistant."},
            {"role": "user", "content": f"Text: {ocr_text}\nCurrent Date: {current_date}"}
        ],
        response_format={"type": "json_object"}
    )
    return response.choices[0].message.content

Sources / references

Contribution Metadata

  • Last reviewed: 2026-06-26
  • Confidence: high