PRODUCTION NOTES · #02ARABIC × AI
Prompt in the language you can check
Published 1 min read
The best language to prompt AI in isn’t always English. It’s the language you can catch its mistakes in.
I noticed this while vibe coding: some things I explain better in Arabic, and the model gets me faster.
Two kinds of developers:
- 01Strong EnglishPrompt in English: fewer words, fewer tokens.
- 02Intermediate EnglishThe risk isn’t your prompt. It’s the reply. The AI proposes a plan full of half-understood terms. You hit “Accept”. It drifts, and you can’t tell.
Research agrees:
- [1]A 2025 study of beginners: non-native English speakers understood prompts less well, and over-trust in AI output was a key barrier.
- [2]Students prompting in Arabic, Chinese and Portuguese solved tasks in their language but struggled with technical terms.
My advice for group 2
- Think in Arabic, keep terms in English (“add auth middleware”, not a translation).
- Mix: English by default, Arabic only where you can’t be precise.
- Add a standing instruction: “explain your plan in simple English at my level.” New terms, learned in context.
The trade-off
Non-English text often costs more tokens (up to 15× across languages, NeurIPS 2023). Anthropic’s benchmarks: Arabic ~97% of English on Sonnet 4.5, ~92.5% on the smaller Haiku 4.5. (See #01: small models expose gaps.)
Arabic is a bridge to group 1, not a place to stay.
Prompt in the language you can verify in.
Sources
- 01“I Would Have Written My Code Differently”: Beginners Struggle to Understand LLM-Generated Code (2025)arxiv.org
- 02Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners (2024)arxiv.org
- 03Petrov et al., Language Model Tokenizers Introduce Unfairness Between Languages, NeurIPS 2023arxiv.org
- 04Anthropic, Multilingual supportplatform.claude.com