Tutorials, guides, and insights about PDF generation
Three practical approaches to extracting structured data from PDFs into JSON: regex on raw text, template-based extraction, and AI-powered extraction with code for each.
Build an automated invoice processing pipeline that turns raw transaction data into branded PDF invoices. Complete working example with HTML template and API integration.
A practical guide to parsing PDFs for retrieval-augmented generation. Covers chunking strategies, PyMuPDF vs Marker vs LlamaParse, and code for extracting and embedding PDF content.
A practical guide to PDF OCR: how to check if a PDF actually needs OCR, Tesseract vs cloud APIs, and when you should skip OCR entirely by generating PDFs with real text layers.
A complete tutorial for building a Python document pipeline that queries a database, formats data with Jinja2, generates PDFs via API, and delivers them via email or S3.
A hands-on comparison of five ways to extract tables from PDFs in Python: pdfplumber, Camelot, Tabula, AWS Textract, and manual regex. With code, benchmarks, and honest pros and cons.
The hidden costs of cobbling together Puppeteer, pdfcpu, and Ghostscript for PDF tasks. How a single API replaces your entire PDF toolchain.
A head-to-head comparison of Kreuzberg, PyMuPDF, and pdfplumber for Python PDF parsing. Benchmarks, architecture differences, and code examples to help you pick the right tool.
An honest comparison of AWS Textract, Google Document AI, Adobe PDF Extract, and open-source alternatives for PDF text extraction in 2026.