Book: Prompt Engineering Pocket Guide: Techniques for Getting the Most from LLMs

Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go

My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools

Me: xgabriel.com | GitHub

A finance team at a mid-sized SaaS feeds 40,000 expense receipts a month

through Tesseract. Most are German supermarket prints with thermal-paper

fade. The accuracy floor on those receipts hovers around 60 percent

character-correct. The team's pragmatic answer in 2024 was a manual

review queue. The 2026 answer is a vision model wired in as a fallback

on the pages Tesseract is not confident about.

That second answer is cheaper than the first, more accurate than running

Tesseract alone, and a fraction of the cost of routing every page

through a VLM. The trick is the routing.

What each tool is actually good at

Tesseract has been the open-source default for over a decade.…