AI Coding Tip 002 - Speak the Model’s Native Tongue

• AI models trained on 90% English data perform better with English prompts for coding tasks
• Non-English prompts waste tokens on translation and create weaker technical solutions
• Mixed-language prompts confuse AI assistants and reduce code generation accuracy

Here's a plot twist that might bruise some linguistic pride: your AI coding assistant secretly prefers English, even if it politely pretends to understand your beautiful native tongue. This isn't cultural imperialism—it's just math. When AI models learned to code, they gorged themselves on a diet that was over 90% English documentation, Stack Overflow answers, and GitHub repositories.

The practical implications are more serious than hurt feelings. When you ask for React hooks in Spanish or SQL optimizations in Mandarin, you're essentially forcing your AI assistant to play telephone with itself. It has to translate your request, figure out what you actually meant (spoiler: "retrollamada" doesn't quite capture the nuance of "callback"), generate code, and then translate back. Each step burns precious tokens and introduces opportunities for the AI to misinterpret your intent.

This revelation matters because it exposes a fundamental asymmetry in how AI processes technical versus conversational language. While these models can chat about philosophy in dozens of languages, their coding neurons fire most efficiently in English. For developers whose native language isn't English, this creates an interesting trade-off: swallow some linguistic pride to get better code, or stick with your mother tongue and accept suboptimal results. The choice seems obvious, but old habits die hard.

The broader lesson extends beyond coding prompts to how we think about AI communication in general. As these tools become more sophisticated, understanding their training biases becomes crucial for extracting maximum value. It's not about the AI being discriminatory—it's about recognizing that even artificial intelligence has preferred languages for specific tasks, just like humans do.