To use an AI API for document summarization and information extraction, you usually follow this workflow:

1) Prepare the document

• Convert the document to plain text if possible.
• If it’s a PDF, DOCX, webpage, or scanned image:
  • PDF/DOCX/HTML: extract text with a parser
  • Scanned PDFs/images: use OCR first

2) Choose the task

Typical tasks:

• Summarization: create a short summary, bullet summary, or executive summary
• Extraction: pull out structured fields like:
  • names
  • dates
  • amounts
  • clauses
  • key topics
  • entities
  • action items

3) Send a prompt to the AI API

You give the model:

• the text
• instructions on what output you want
• optionally a structured format like JSON

Example prompt for summarization:

Summarize the following document in 5 bullets. Focus on key decisions, risks, and action items.

Example prompt for extraction:

Extract the following fields from the document and return JSON:
- title
- date
- parties
- total_amount
- key_obligations
- deadlines

4) Get structured output

For extraction, ask for JSON so your app can parse it reliably.

Example output:

{
  "title": "Contract Agreement",
  "date": "2026-10-08",
  "parties": ["Company A", "Company B"],
  "total_amount": "$50,000",
  "key_obligations": ["Deliver software", "Provide support"],
  "deadlines": ["2026-11-01"]
}

5) Handle long documents

If the document is too long for one request:

• split it into chunks
• summarize each chunk
• combine the chunk summaries into a final summary
• for extraction, extract per chunk and merge results

This is often called a map-reduce or chunk-and-aggregate approach.

---

Basic example

Python-style pseudocode

import requests

api_key = "YOUR_API_KEY"
text = open("document.txt").read()

prompt = f"""
Summarize this document in 5 bullet points:
{text}
"""

response = requests.post(
    "https://api.example.com/v1/generate",
    headers={"Authorization": f"Bearer {api_key}"},
    json={
        "model": "your-model-name",
        "prompt": prompt
    }
)

print(response.json())

---

Better example for extraction

prompt = f"""
Extract the following from the document and return valid JSON only.

Fields:
- invoice_number
- vendor
- invoice_date
- due_date
- total
- line_items

Document:
{text}
"""

You then parse the JSON response in your code.

---

Best practices

• Be explicit: say exactly what summary/extraction format you want
• Use JSON schema when possible: reduces parsing errors
• Chunk large docs: avoid exceeding token limits
• Validate outputs: especially for dates, totals, and IDs
• Add examples: few-shot examples improve accuracy
• Use retries: handle malformed JSON or timeouts
• Redact sensitive data if needed

---

Common document workflows

Summarization

• Executive summary
• Section-by-section summary
• Meeting notes summary
• Legal document summary
• Research paper abstract

Extraction

• Contract clause extraction
• Invoice/receipt fields
• Resume parsing
• Medical record fields
• Named entity extraction
• Key-value extraction from forms

---

If you want a production setup

A robust pipeline usually looks like this:

1. Upload document
2. Extract text / OCR
3. Clean text
4. Chunk by section/page
5. Call AI API for each chunk
6. Merge results
7. Validate output
8. Store results in DB / search index

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If you want, I can also show you:

• a real Python example using OpenAI API
• a Node.js example
• a JSON-schema extraction prompt
• a workflow for PDFs and OCR