Why AI Acts Different in Other Languages (And What to Do About It)

Breaking down language barriers: Lessons from Non-English speaking AI users

Picture this: You're confidently chatting with an AI in English about your latest project. The responses are nuanced, helpful, exactly what you need. Now imagine your colleague in Perú does the same thing in Spanish... while the AI responds competently (modern AI is actually quite good with Spanish!), there are subtle differences. Maybe it misses some cultural context, uses overly formal language when casual would be better, or doesn't quite catch local expressions. It's like talking to someone who learned your language perfectly in theory but hasn't lived in your country - they understand everything, but something feels slightly off.

These small differences might seem minor, but they add up. While multilingual users can definitely use AI effectively, getting that same natural, flowing conversation you experience in English often requires some smart techniques. And that's exactly what non-English speaking communities have figured out.

The English-First Brain: Understanding AI's Language Bias

Why does this happen? It all comes down to AI's 'education.' Here's a way to think about it: Imagine you're dealing with a highly skilled chef who trained primarily in American cuisine. Sure, they can make excellent dishes from other culinary traditions - their ceviche might even be pretty good! - but their hamburgers will always have that extra special touch that comes from deep, native expertise.

That's similar to how modern AI models work with different languages. They're actually quite competent in Spanish, Chinese, Arabic, and many other languages - we're way past the broken translations of the 90s. But there's often a subtle difference in quality, like a chef whose food is delicious across cuisines but shows particular brilliance in their specialty.

This creates an interesting situation: while these AI models are genuinely multilingual and can handle complex tasks in many languages, their 'digital brain' still has a slight preference for English patterns. It's not about basic understanding anymore - modern AI handles that well - it's about catching subtle cultural nuances, understanding regional expressions, and matching the natural flow of how ideas are expressed in different languages.

The good news? There are clever techniques that can help bridge even these small gaps.

Two Smart Techniques

When non-English speakers started experimenting with AI, they made a fascinating discovery: even though AI models can work directly in their languages, they found they could get even better results by working with the AI's 'English-first' nature rather than against it. This led to two techniques that not only improve their results but also give us interesting insights into how AI models actually process language:

1. The 'Translate First' Technique

Think of this like having a bilingual expert on your team. Instead of forcing the AI to think directly in, let’s say Spanish, users first ask it to translate their question to English, process it in English, and then translate it back. It sounds counterintuitive, but it often produces more nuanced, contextually aware responses.

2. The 'Cross-Lingual Thought' Technique

This more advanced approach is like giving the AI explicit permission to use its full linguistic capabilities. Users actually write their prompts in English but keep their questions in their language, essentially telling the AI 'Yes, I know you think best in English, so let's use that to our advantage.'

What makes these techniques particularly interesting isn't just that they work - it's what they reveal about how AI models process language. Even when an AI can work competently in multiple languages, it still has this 'English-centric' way of thinking under the hood.

Let's see how this works in practice with a real example that reveals why this technique is so powerful.

Imagine an environmental science student in Peru wants to understand the impact of Amazon deforestation. Here's the fascinating difference between two approaches:

Standard Approach: They might directly ask in Spanish: '¿Cuál es el impacto de la deforestación en la Amazonía sobre la biodiversidad y el clima global?' While they'll get a competent answer, it might miss some of the latest research or specific examples that are more readily available in English.

The 'Translate First' Technique: Here's their enhanced prompt:

Please follow these steps:
1. Translate this question to English: ¿Cuál es el impacto de la deforestación en la Amazonía sobre la biodiversidad y el clima global?
2. Answer the question in English, including recent scientific data and specific examples of affected species
3. Translate your response to Spanish, ensuring precise ecological terminology and mentioning relevant conservation initiatives in Amazonian countries

Why does this work better? Because it:

• Taps into the AI's larger English-language scientific database

• Ensures technical terms are handled accurately in both languages

• Maintains cultural relevance while accessing global research

• Preserves local context while drawing from international sources

This might seem like extra work, but Spanish speakers have found that this 'bilingual bounce' often produces richer, more precise responses, especially for technical or academic topics.

The Second Technique: 'Thinking in Both Languages' Here's where things get really interesting. These users made an interesting observation: if AI models think best in English but can understand their languages perfectly well, why not let them use both languages simultaneously?

This led to what they call 'Cross-Lingual Thought' (XLT), and it's brilliantly counterintuitive. Instead of writing everything in Spanish, they write their instructions in English but keep their actual content in their native language. It looks something like this:

I want you to act as an ecology expert for Spanish speakers. 

Question: ¿Cuál es el impacto de la deforestación en la Amazonía sobre la biodiversidad y el clima global? 

Please follow these steps: 
1. First, mentally process this question in English 
2. Plan your response considering global research and data 
3. Deliver your answer in natural, fluent Spanish 
4. Ensure you include region-specific examples

Why is this cool? Because it:

• Directly acknowledges and works with the AI's English-centric training

• Keeps the content in Spanish, maintaining cultural context

• Gives specific instructions about how to think about and process the information

• Results in responses that feel more natural than simple translations

Think of it like giving a bilingual expert specific instructions about how to use their language skills. Instead of pretending the AI doesn't have an English-centric background, this technique embraces it while ensuring the output maintains its Spanish character.

This approach reveals something fascinating about how AI language models work - they're not just translation engines, but complex systems that can think in multiple languages simultaneously when properly guided.

Understanding the Limitations: What These Techniques Tell Us About AI's Language Challenge

While these techniques are innovative solutions, they also reveal some fascinating challenges about AI:

1. The Cultural Context Gap Original limitation: Non-English speakers noticed that translation-based approaches sometimes miss cultural nuances. Broader insight: This reveals how AI still struggles with cultural context in any language. Even in English, AI might miss cultural subtleties - it's just less noticeable when working in the AI's 'native' language.

2. The Processing Trade-off: These bilingual techniques take longer than direct interaction. This highlights how multilingual users often need to put in extra effort to get the same quality of results that English speakers get naturally.

3. Technical Terminology Challenges: Users often request terms in both languages. This shows how technical knowledge in AI systems still has an English bias, affecting global professional and academic work.

4. Knowledge Base Limitations: Even with these techniques, the AI's knowledge of local contexts remains limited. As AI becomes more global, we need to think about how to make its knowledge base more culturally diverse.

Why This Matters for Everyone:

• As AI becomes more prevalent globally, understanding these limitations helps create better cross-cultural collaboration

• English speakers working with international teams can better understand their colleagues' AI experiences

• These insights can help us build more inclusive AI systems in the future

What These Innovations Tell Us About AI's Future

The techniques we've explored aren't just clever hacks - they're early signals of how AI might evolve to become truly multilingual. Here's what the Spanish-speaking community's experiences suggest about the future:

1. Regional AI Adaptation: Users are developing region-specific prompting techniques. We might see AI models that automatically adapt to different cultural and linguistic contexts - imagine an AI that naturally switches between American English, British English, and Indian English styles.

2. Cultural Intelligence: Users are teaching AIs to understand cultural contexts, this could mean that future AI models might seamlessly handle cultural references across all languages, not just translate words.

3. Community-Driven Solutions: The community discovered these techniques through experimentation.

The non-English speaking community has shown us that the path to better AI isn't just about technological advancement - it's about understanding and working with AI's current limitations while pushing for a more inclusive future.

A New Way to Think About AI and Language

If you're an English speaker, this journey through AI techniques might seem like looking through a window into someone else's experience. But it's actually more like a mirror - reflecting something important about AI that we don't usually notice.

Key Takeaways:

• AI's English-centric nature isn't just a challenge for others - it's a reminder that AI still has significant limitations in how it processes human communication

• These techniques show us that there's often more than one way to work with AI

• These insights become increasingly valuable as AI becomes more prevalent in global and multicultural settings

What You Can Do:

1. Next time you use AI, try to notice how it handles cultural references and context - even in English

2. If you work with international colleagues, share these insights about language-specific techniques

3. Consider how your AI prompts might work (or not work) in other languages

The Future Is Multilingual: As AI continues to evolve, the insights other language communities will help shape more inclusive and effective AI systems for everyone. Their experiences remind us that the future of AI isn't just about technological advancement - it's about understanding and bridging the gaps between different ways of thinking and communicating.

The next time you have a smooth interaction with AI, remember: users around the world are actively shaping how we all interact with it, making it work better for everyone, one language at a time.

See you next time!
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Hey! I'm Germán, and I write about AI in both English and Spanish. This article was first published in Spanish in my newsletter AprendiendoIA, and I've adapted it for my English-speaking friends at My AI Journey. My mission is simple: helping you understand and leverage AI, regardless of your technical background or preferred language. See you in the next one!