Why Polish Is Currently the Best Language for AI Prompting

The assumption has always been straightforward: English is the lingua franca of the internet, so it must be the native tongue of Artificial Intelligence. Since the vast majority of training data—from Common Crawl to GitHub repositories—is in English, models like GPT-4 or Gemini should naturally "think" best in English.

A recent study involving researchers from the University of Maryland and Microsoft has upended this logic. When put to the test in specific retrieval tasks, Polish AI prompting outperformed English, despite Polish having a fraction of the training data.

This finding suggests that the architecture of a language matters more than the volume of text fed into the model. For developers and prompt engineers, this shifts the conversation from "how much data do we have?" to "how precise is the language we are using?"

The OneRuler Benchmark: How Polish Crushed the Competition

To understand why Polish AI prompting took the top spot, we have to look at how the models were tested. The researchers used the OneRuler benchmark, a test designed to stress-test an LLM's ability to retrieve specific information from a massive dataset—a digital version of finding a needle in a haystack.

In these tests, the AI is given a "context window" (the amount of text it can look at once) ranging from 4k to 64k tokens. The goal is long-text retrieval: finding a specific relationship or fact buried deep within that text.

English, typically the gold standard, achieved an accuracy score of roughly 83.9% on the higher end of the context length. Polish scored 88%.

Why AI Prompting in Polish Handles Context Length Better

The disparity becomes even more interesting when you look at context length. As the amount of text grows, most models hallucinate or lose the thread of the logic. English prompts often suffer from "context drift"—the model forgets who did what to whom because the subject and the object are separated by thousands of words.

Polish held its integrity better over long distances. The reason lies in the mechanics of the language itself. In the Reddit discussions surrounding this news, users pointed out that Polish is a highly "inflected language." This means the role of a word (subject, object, instrument) is encoded directly onto the end of the word itself.

In English, "The dog bit the man" means something different than "The man bit the dog" purely because of word order. If you separate "man" and "dog" by ten paragraphs of text, the AI might get confused about who did the biting. In Polish, the word endings for "man" and "dog" would clarify the biter and the victim regardless of where they sit in the sentence.

It’s Not About Data Volume: The Semantic Density Factor

The OneRuler results highlighted a concept known as semantic density. This measures how much logical information is packed into a single token.

English is considered an "isolating" language. It relies heavily on "helper words"—articles (a, the), prepositions (of, for, with), and auxiliary verbs (do, have). These words take up token space but often don't add unique semantic value; they just provide the glue.

Polish AI prompting is more efficient because the language is synthetic. It packs the "glue" into the words themselves. This higher semantic density means that within a limited context window (say, 32k tokens), a Polish prompt can convey more complex logical relationships than an English prompt.

Inflected Languages and the Future of AI Prompting

This study isn't just a win for Poland; it’s a validation for inflected languages in general. The study found that other languages with complex grammatical structures—like French and Ukrainian—also punched above their weight class relative to their training data size. Conversely, Chinese, which relies heavily on context and has little inflection, struggled in these specific retrieval benchmarks despite having massive training datasets.

For the AI, an inflected language acts like a checksum. It reduces ambiguity. When a model processes a Polish sentence, it doesn't have to guess the relationship between words based on proximity; the relationship is hard-coded into the morphology. This suggests that for tasks requiring high precision and logic, the "best" language might not be the one with the most websites, but the one with the strictest rules.

Personal Experience: Applying "Polish Logic" to Your English Prompts

You likely don't speak Polish, and you probably aren't going to learn it just to get slightly better answers from ChatGPT. However, studying why Polish AI prompting works allows us to reverse-engineer better English prompts.

The core advantage of Polish is the elimination of ambiguity through explicit relationship markers. We can simulate this in English. I’ve started applying these "Polish principles" to complex analysis tasks, and the hallucination rate drops significantly.

Simulating Polish Precision in AI Prompting

Here is a guide on how to structure English prompts to mimic the structural advantages of high-density languages.

1. Force Explicit Subject-Object RelationshipsIn casual English, we often rely on implied context.Standard Prompt: "Look at the financial reports and tell me which ones are losing money and why."The Issue: "Ones" is vague. The AI has to infer if you mean the reports, the companies, or the departments.The "Polish Logic" Fix: "Analyze the provided financial reports (Source). Identify specific subsidiaries (Subject) that reported a net loss (Condition). For each identified subsidiary, extract the stated reason for the loss (Object)."

2. Reduce Helper Word "Noise"Since semantic density is key, cut the fluff that confuses the tokenizer.Standard Prompt: "I want you to go ahead and make a list of all the people who might be relevant to the case."The "Polish Logic" Fix: "List all case-relevant individuals."By condensing the request, you increase the density of instruction-per-token, leaving less room for the model to drift.

3. Use Tagging for "Artificial Inflection"Since English lacks case endings, use XML tags or brackets to lock relationships in long texts. This is a common long-text retrieval hack.Strategy: When asking an AI to process a 50-page document, don't just say "Find the breach date."Prompt: "Scan the text for the event [DATA_BREACH]. Extract the attribute [DATE] associated specifically with [DATA_BREACH]. Ignore dates associated with [REPORT_PUBLICATION]."You are essentially creating your own grammatical cases to tell the AI exactly which date belongs to which event.

Community Reactions: Is This Just a Benchmark Fluke?

It is important to look at these findings with a critical eye. The Reddit community discussing the Euronews article raised valid points about the limitations of the study.

One major point of contention was the source material. Some users speculated that the Polish AI prompting results might be skewed by the specific books or datasets used in the OneRuler benchmark. If the Polish texts used for testing were translated from English with high precision, or if they were simpler literary texts, it could affect the score.

Others pointed out the "Tokenizer Bias." Different models chop words up differently. If the tokenizer splits a Polish word into three parts but keeps an English word as one, the "efficiency" argument gets complicated. However, the study suggests that even with tokenization differences, the informational efficiency of Polish remains superior.

There is also the reality of "Low Resource" vs. "High Resource." Users noted that while Polish is technically a "lower resource" language compared to English, it isn't obscure. It has a massive corpus of literature and technical writing. The surprise wasn't that Polish worked, but that it beat the languages that the models were effectively "raised" on.

This debate highlights that while English is currently the default interface for AI, it is structurally imperfect for the way Neural Networks process logic. English is full of idioms, silent letters, and order-dependent meanings—all things that add computational overhead.

The Future of Prompting Languages

We might be heading toward a future where we don't prompt in natural languages at all, or perhaps we choose specific languages for specific tasks.

If you need creative writing or a poem, English or Italian might remain superior due to their vast stylistic training data. But for logic, legal analysis, or complex code generation logic, we might see a shift. It’s not inconceivable that backend systems will translate user queries into an intermediate, highly inflected language (like a synthetic version of Polish or even Latin) to process the logic before translating the answer back to the user.

Polish AI prompting success is a signal that our assumptions about "more data = better performance" are hitting a wall. The structure of the data—and the language that carries it—is the new frontier for optimization.

FAQ: Polish and AI Prompt Engineering

1. Why does Polish perform better than English in AI prompting?

Polish is an inflected language with complex grammar cases, which allows for precise relationships between words without relying on word order. This structure reduces ambiguity for the AI, especially in long-text scenarios, leading to higher accuracy in retrieval tasks.

2. Does this mean I should translate my prompts into Polish?

Not necessarily, unless you are fluent. However, for complex long-text retrieval tasks where English fails, using a translation tool to prompt in Polish and then translating the result back has been shown by some power users to yield more logical results.

3. What is the OneRuler benchmark mentioned in the study?

OneRuler is a benchmark designed to test the effective context length of Large Language Models (LLMs). It measures how well an AI can retrieve specific, "needle-in-a-haystack" information from varying lengths of text, ranging from short paragraphs to novels.

4. How does semantic density affect AI performance?

Semantic density refers to how much meaning is packed into a single token or word. High-density languages like Polish convey more information with fewer "filler" words (like 'a', 'the', 'do'), allowing the AI to maintain a clearer logical thread over long contexts.

5. Are other languages better than English for AI?

The study suggests that other inflected languages like French and Ukrainian also perform surprisingly well. However, Polish showed the most significant lead in the specific retrieval tasks analyzed, despite having less training data than English or Chinese.

6. Is English bad for AI prompting?

English is not "bad"—it is still the most versatile due to the sheer size of its training data. However, its reliance on word order and high usage of helper words makes it less efficient for strictly logical retrieval tasks compared to highly structured languages.