Seven cases, three genders and consonant alternation. Polish inflection breaks tokenisation designed for English. What that means for your AI deployment.
Ask a general-purpose model to write a formal letter in Polish and something goes wrong that is hard to name if you do not speak the language and impossible to miss if you do. It is fluent enough to pass a non-speaker's review and wrong enough that no Pole would send it.
The cause is structural. Polish inflects across seven cases, verbs conjugate across aspect, stems alternate, word order carries information, and English-trained subword tokenisation fragments inflected forms against morpheme boundaries.
In a real deployment, retrieval fails on inflection, intent classification degrades on paraphrase, and generation errors are invisible to non-speakers, which is the dangerous one: a non-native reviewer passes output a customer will reject.
What works: morphological analysis and lemmatisation before indexing and intent matching, evaluation sets written in Polish rather than translated, native-speaker evaluation with a linguistic error taxonomy, and retrieval grounding for anything consequential.
Frequently asked questions
- Why do AI models perform worse in Polish than in English?
- Polish is highly inflected, so a single word generates many surface forms. English-trained subword tokenisation fragments them unhelpfully, and Polish training data is far scarcer.
- What goes wrong in a Polish chatbot deployment?
- Retrieval fails when an inflected query does not match differently inflected documents, intent classification degrades, and errors in case, aspect and register are invisible to non-native reviewers.
- Can a non-Polish speaker assess Polish AI output quality?
- Not reliably. Fluent but grammatically incorrect Polish reads as acceptable to a non-native reviewer and as obviously wrong to a native speaker. It requires native-level evaluators.