Urdu AI chatbot: talk to AI in Urdu script or Roman Urdu

Most AI products will produce Urdu if you ask them to. Whether they do it well is a different question, and the answer depends on three separable things that get lumped together: whether the model understands Urdu, whether the interface displays it correctly, and whether the product knows anything about the context Urdu is used in.

A product can do one of these well and the others badly, and it usually does. Here is what each one actually requires, how to tell which is failing, and how to get better Urdu out of any assistant.

Understanding: why Urdu is a lower-resource language for models

Language models learn from text, and there is far less digitised Urdu text than English text — by orders of magnitude, not percentages. Much of the Urdu that exists online is transliterated, machine-translated, or of low editorial quality, which is worse than absent because it teaches wrong patterns.

Tokenisation compounds it. Models split text into subword units learned mostly from high-resource languages, so an Urdu word tends to fragment into many small tokens instead of one or two clean ones. That costs context budget — the same paragraph consumes several times more of the model's working memory in Urdu than in English — and it gives the model a noisier representation to reason over.

The practical result is a capability gradient that most users notice without being able to name: simple Urdu conversation is fine, complex Urdu reasoning is weaker than the same reasoning in English, and formal or literary Urdu is weakest of all. A useful technique that follows directly from this is to ask hard questions in English and request the answer in Urdu — you get English-level reasoning with Urdu output, which is better than Urdu-level reasoning.

Display: the part that is a software problem, not a model problem

Urdu is written in Nastaliq, a cursive style with a steeply sloping baseline where letters join and change shape by position. Arabic is conventionally set in Naskh, which is more horizontal and more angular. They use overlapping character sets, so a system that has an Arabic font but not an Urdu one will render Urdu text without any error — just in a style that is technically legible and reads, to an Urdu reader, as wrong.

This is why "the Urdu looks strange" is such a common complaint even when the words are correct. It is a font-loading problem in the interface and has nothing to do with the model.

Right-to-left layout is the second half of it, and it is harder than flipping a text direction. Mixed content is the difficulty: an Urdu sentence containing an English product name and a number has three directional runs in it, and getting the punctuation, the parentheses and the cursor behaviour right requires actually implementing bidirectional text rather than setting a CSS property. Interfaces that get this wrong put the full stop at the wrong end of the sentence, which every Urdu reader sees immediately.

Context: knowing what the words refer to

This is separate from language and is routinely mistaken for it. A model can parse your Urdu perfectly and still answer uselessly because it does not know the world you are asking about.

Intermediate boards and their marking conventions. FSc versus A-levels as parallel tracks with different consequences. What a NayaPay transfer is. How a Pakistani utility bill is structured. Which institutions are which. Models trained overwhelmingly on English-language internet text hold all of this thinly, and they fill gaps with the closest thing they know, which is usually American or Indian.

The workaround is mundane and effective: state the context in one clause. "For a Sindh board intermediate student" or "in Pakistan, paying by bank transfer" costs you six words and removes an entire category of confidently wrong answer.

Getting better Urdu output from any assistant

• Specify the script explicitly. "Urdu script" and "Roman Urdu" are different requests, and input language does not imply output language.
• Specify the register. Urdu has a much wider formal-to-informal range than English, and "formal Urdu, as for an application to a government office" produces something quite different from an unqualified request.
• Ask hard questions in English, request Urdu output. This works because reasoning quality and output language are independent.
• For anything you will send or submit, generate it and then read it aloud. Urdu output errors are more often about register and naturalness than grammar, and reading aloud catches those where reading silently does not.
• Keep English technical terms in English rather than accepting a translation. Translated technical vocabulary in Urdu is frequently non-standard and can be actively confusing to a reader who knows the field.
• For poetry and anything where sound matters, work in Urdu script rather than Roman Urdu. Roman transliteration discards vowel information that prosody depends on.

What to expect, honestly

Conversational Urdu, explanations, summaries and everyday correspondence are handled well by current models. Formal correspondence is usually good with an explicit register instruction. Technical and academic Urdu is inconsistent, largely because the standard vocabulary itself varies between institutions. Literary Urdu and poetry are where models are weakest and where a fluent reader will immediately see the difference.

Nobody has solved Urdu. Products that are better at it are better by degree, and the honest framing is that this is improving rather than done.

Where RafayGen stands on each of the three

On display, which is the fully solvable one: Urdu output renders in proper Nastaliq rather than a fallback Naskh font, and the interface implements right-to-left layout including mixed-direction content. This is a straightforward engineering commitment and it is the difference an Urdu reader notices first.

On understanding: input is accepted in English, Urdu script and Roman Urdu interchangeably, including mixed within one sentence, with no language toggle — the language is inferred from what you typed. Voice works in Urdu and English and follows you when you switch mid-conversation. Urdu OCR handles scanned Urdu material. All of this sits on top of the same third-party models everyone else uses, so the capability gradient described above applies here too.

On context: being built and operated from Pakistan means the local defaults are the defaults rather than an override. That is a real advantage and it is also the narrowest of the three — it helps with the assumptions baked into the product, not with what the underlying model knows.