When Language Sounds Off
Large language models often fail to sound natural in Portuguese and Spanish, not because of lack of fluency, but due to deep cultural, pragmatic, and contextual mismatches. This article explores how context engineering, cultural awareness, and linguistic insight can help bridge that gap and produce outputs that truly resonate with real-world users.
Language, Culture and Language Models
TL;DR
- LLMs often sound unnatural in Portuguese and Spanish.
- They’re trained mostly on formal and English-heavy text.
- Language is culture, tone, and intent, not just tokens.
- Context matters more than syntax alone.
- Formality is not the same as politeness.
- Directness is not rudeness in many cultures.
- Without pragmatic awareness, models misalign.
- The solution is context engineering and linguistic insight.
- Models must speak like users, not like textbooks.
Introduction
Large Language Models (LLMs) have rapidly become the centerpiece of modern AI, fueling everything from chatbots and search engines to research tools and code assistants. Their promise is bold: to “understand and generate language” at a near-human level.
But what exactly do we mean by “language”?
Most discussions around LLMs reduce language to tokens, statistical patterns, and textual outputs. While this approach yields practical results, it often ignores the deeper layers of what language truly is, not just a sequence of words, but a rich interplay of context, culture, tone, intention, and interaction. Language is not just what is said, but how, why, and when it is said.
LLMs are, at their core, mathematical models trained on linguistic data. Yet, they are expected to function in real-world conversations, across cultures, registers, and discourse types. This tension, between language as a lived human phenomenon and language as a statistical abstraction, is one of the most overlooked challenges in current AI development.
This article explores the intersection between linguistics and language modeling, examining how insights from phonology, pragmatics, and sociolinguistics can inform the development of better, more culturally aware language systems. We’ll investigate the hidden biases in LLM outputs, especially when applied to languages like Brazilian Portuguese and Latin American Spanish, and argue that without an understanding of contextual variation and pragmatic nuance, even the most sophisticated models will sound unnatural, or worse, inappropriate.
What’s emerging is a new frontier: context engineering, the art and science of shaping how LLMs understand, adapt, and respond to communicative context. And those who understand language in its full, lived complexity have a unique opportunity to shape the future of this field.
The Structure of Language: Complexity and Balance
One of the most fascinating aspects of human language is that it distributes complexity in a remarkably balanced way. Languages may differ in what they make complex: sound systems, grammar, word order, tone, morphology. But few, if any, are complex in all areas at once. Instead, they tend to compensate: a language with a rich morphology might have a simpler phonology; a tonal language may use fewer consonants; a language with rigid word order may reduce the need for case markings.
This pattern is not random. It reflects a kind of communicative equilibrium, a balance that allows languages to remain cognitively efficient while adapting to cultural, historical, and social constraints. Linguists refer to this phenomenon as a form of functional load distribution or complexity trade-off.
Take, for example:
- Sanskrit, which has one of the most elaborate consonant systems in human history, 33+ phonemic consonants with fine-grained articulatory distinctions, but a relatively stable and symmetrical vowel system.
- English, on the other hand, has a relatively average set of consonants but an unusually large and irregular vowel inventory (including diphthongs and tense/lax contrasts), contributing to its infamous spelling-to-sound unpredictability.
- Japanese offers a minimalist phoneme inventory, especially in terms of consonants and vowels, yet compensates with a fast syllabic rate and a rigid pitch-accent system.
- Modern Greek has relatively few phonemes, but a morphosyntactic system that is historically layered and semantically dense.
These trade-offs don’t make any language “better” or “worse”, they simply reflect how each language solves the problem of communication using different strategies.
The Science of Speed vs. Density
A remarkable study by Coupé et al. (2011) quantified this principle using two metrics:
- Syllables per second (how fast a language sounds)
- Information per syllable (how much content each syllable carries)
Their finding? Languages like Japanese and Spanish are spoken quickly, but each syllable carries less information. Languages like Mandarin or German are spoken more slowly, but their syllables are more content-rich.
And yet, when you multiply the two, the total information conveyed per second remains strikingly consistent across languages (roughly 39–41 bits/second).
This insight is crucial when we consider LLMs. Because while models can be trained on textual tokens, they are ultimately modeling human communication, which is optimized for efficiency, not surface complexity.
What we perceive as “verbosity” or “simplicity” is often the result of these underlying trade-offs. If LLMs are blind to them, they risk sounding off-tone, robotic, or awkward, especially when crossing linguistic and cultural boundaries.
Context Matters: Beyond Text and Tokens
The idea that language is reducible to text is a seductive illusion, especially in the world of machine learning. After all, most LLMs are trained on massive corpora of written language, tokenized, stripped of prosody, context, and speaker intention. The result is a model that predicts words based on frequency and co-occurrence, but not necessarily meaning as humans experience it.
Language, however, is not just a sequence of symbols. It is a social act.
We speak differently to a friend than to a doctor. We phrase a request differently depending on power dynamics, emotional tone, or urgency. We code-switch between registers, dialects, and even languages depending on identity and context. And we often say things that mean something other than what the words literally express.
These layers, pragmatics, register, discourse structure, social deixis, intonation, are mostly invisible to LLMs unless explicitly modeled. The models may learn some statistical patterns that correlate with polite language or informal expressions, but they don’t understand why or when these choices matter.
This gap is especially noticeable when LLMs are deployed in real-world settings where naturalness, appropriateness, and humanlike interaction are key. Consider the following examples:
- A model that responds to a casual WhatsApp message with a formal, verbose paragraph.
- An assistant that over-apologizes in Brazilian Portuguese, using phrases that sound servile or artificial in context.
- A chatbot that fails to adjust tone between a support request and a legal inquiry, using the same sentence structure for both.
In all these cases, the model may be grammatically correct, even semantically accurate. But it fails at communicative alignment, the core of successful human interaction.
Context Engineering: A New Frontier
This is where a new field is emerging: context engineering. It goes beyond prompt engineering to encompass:
- Modeling speaker–listener relationships
- Embedding cultural expectations
- Adapting to discourse genre and channel (email, chat, documentation)
- Calibrating tone, formality, and conciseness
Context engineering acknowledges that how something is said is often more important than what is said. It requires not just data, but linguistic awareness, cultural sensitivity, and intentional design.
Ironically, the more powerful LLMs become, the more important this layer becomes. As they approach humanlike fluency, their failure to model humanlike appropriateness becomes more jarring. And the solution is not more data, but smarter context.
The Case of Brazilian Portuguese and Latin American Spanish
When it comes to language models, not all languages are treated equally, and not all language cultures are modeled with the same fidelity.
Brazilian Portuguese and Latin American Spanish offer a striking example of what happens when LLMs trained primarily on formal written text are deployed into informal, spoken, and culturally specific communication contexts.
The Split Between Written and Spoken Norms
Unlike English, where written and spoken registers often share a close structure (especially in informal use), both Portuguese and Spanish exhibit a strong diglossia. The way people write, especially in books, articles, and formal emails, is significantly different from how they speak, even in casual written formats like messaging apps.
For example:
- In Brazilian Portuguese, the written phrase:
“Agradeço desde já pela sua atenção”
Lit. “I thank in advance for your attention”; meaning: “Thank you in advance” is common in emails but would sound oddly stiff if spoken aloud to a friend.
- Real-life spoken interaction might be as direct as:
“Valeu, até mais”
Lit. “Thanks, until later”; meaning: “Thanks, talk to you later”
or“Pode deixar que eu vejo isso.”
Lit. “Can leave it that I see this”; meaning: “Don’t worry, I’ll take care of it”
- In Mexican Spanish, a formal closing like
“Quedo atento a sus comentarios”
Lit. “I stay attentive to your comments”; meaning: “Looking forward to your feedback” is standard in professional emails but would feel overly formal in speech or texting.
- In contrast, casual interaction might use:
“¿Me avisas?”
Lit. “You tell me?”; meaning: “Let me know” or
“Ya lo checo y te digo.”
Lit. “Just I check it and I tell you”; meaning: “I’ll check and let you know”
In many Latin American regions, the written norm still reflects conservative grammatical structures such as vosotros, leísmo, and complex verb tenses, while the spoken form is far more fluid and expressive.
LLMs trained primarily on formal corpora like books, encyclopedias, and institutional websites tend to internalize an elevated register. This often results in outputs that:
- Sound overly verbose or ceremonial
- Use constructions uncommon in everyday conversation
- Fail to match the tone expected in informal or relational communication
This mismatch becomes especially visible in chatbots, support agents, or assistant tools that attempt to mirror the user’s tone but fall short due to register misalignment.
Politeness Norms Are Cultural, Not Universal
Another layer of complexity is pragmatic politeness, which is not a universal template, but a culturally embedded system.
LLMs trained heavily on English corpora often default to:
- Over-apologizing: “We sincerely apologize for the inconvenience caused.”
- Hedging: “If you don’t mind, I’d like to…”
- Excessive softeners: “Would it perhaps be possible to kindly…”
In Brazilian or Rioplatense culture, this level of indirection is often counterproductive, or even seen as evasive or ironic. In these contexts:
- Directness is not impolite; it’s expected.
- Clarity and spontaneity are valued more than formulaic politeness.
- Over-apology can signal weakness, guilt, or detachment.
For example:
“Desculpe, mas creio que houve um equívoco da sua parte.”
Lit. “Sorry, but I believe there was a mistake from your side”; meaning: “I think you made a mistake” it might pass as neutral in Portuguese, while the same phrase in English can feel sharp or even confrontational.
“Acho que você entendeu errado.”
Lit. “I think you understood wrong”; meaning: “I don’t think you got it right” is a common way to correct someone without offense.
In Mexican Spanish, a phrase like:
“Creo que no es así.”
Lit. “I believe that’s not so”; meaning: “I don’t think that’s right” is more common than hedging with “perhaps” or “maybe”.
Meanwhile, “No te preocupes, yo me encargo.”
Lit. “Don’t worry, I take charge”; meaning: “No problem, I’ll handle it” sounds supportive and proactive, without softeners.
The directness in these languages reflects trust, emotional openness, and contextual clarity, not rudeness. That’s why direct translations of English politeness strategies often misfire in Latin American or Iberian contexts.
The Cost of Misalignment
When a model misaligns with the expected tone, users feel it, even if they can’t explain why.
- It breaks the illusion of naturalness.
- It signals “this wasn’t written by someone like me.”
- It creates friction, especially in user-facing tools where tone and social resonance are part of the user experience.
And this is not a limitation of transformer architectures, it’s a design oversight. One that can only be addressed by integrating linguistic, pragmatic, and cultural expertise into the pipeline.
Opportunities and Practical Solutions
While the challenges of linguistic-cultural misalignment in LLMs are real, they are far from insurmountable. In fact, they point toward an emerging design paradigm: one in which linguistic diversity and pragmatic nuance are treated not as noise, but as essential signals in the engineering of natural language systems.
Here are several key avenues where linguistically informed strategies can lead to better performance, usability, and user trust.
1. Fine-tuning and Instruction Tuning with Culturally Grounded Data
LLMs trained predominantly on formal and canonical texts often fail to reproduce the tone and rhythm of everyday language. One solution is fine-tuning on domain-specific, culturally representative datasets, such as:
- Chat logs, customer support interactions, or informal emails
- Regional corpora with diverse dialects and registers
- Crowdsourced conversations with consented tone annotations
This kind of tuning doesn’t just improve linguistic fluency, it enhances pragmatic alignment and trust. A model that speaks like the user is more likely to be accepted by the user.
2. Context-Aware RAG (Retrieval-Augmented Generation)
Retrieval systems often focus purely on factual precision, but the form of the retrieved content also matters.
By curating retrieval sources that reflect local linguistic norms (e.g., Brazilian documentation, Latin American product FAQs, regional policy statements), we can inject contextual tone and terminology directly into generation pipelines.
This approach helps models:
- Adjust style based on domain
- Adopt expected register without needing hardcoded prompts
- Avoid hallucinating unnatural phrasings
3. Pragmatic Evaluation Metrics
Most LLM evaluation still relies on factual accuracy, BLEU-like metrics, or subjective human ratings. But real-world usage calls for pragmatic evaluation:
- Does the model sound like a peer, or like a corporate memo?
- Is the tone appropriate for the context and audience?
- Would a native speaker actually say this?
By incorporating evaluation layers that include tone, register, and cultural fit, we can better detect misalignments, and correct them before deployment.
4. Persona and Voice Design Layers
Instead of relying solely on temperature and prompt hacks, future systems can incorporate modular context layers that define:
- Voice (formality, warmth, conciseness)
- Persona (role, social identity, power dynamic)
- Channel (email vs. chat vs. speech)
These can be controlled explicitly (via metadata or user intent), or implicitly (via interaction history), giving users fine-grained control over how language is shaped, without breaking the illusion of spontaneity.
Ultimately, none of these solutions require reinventing LLMs, they require reframing how we apply them. And that reframing must include linguists, sociolinguists, discourse analysts, and culturally embedded experts as part of the engineering loop.
Conclusion: Toward Culturally Sensitive Language Engineering
Large Language Models are not just tools for generating text, they are interfaces for human communication. And communication is never neutral. It is shaped by culture, context, intention, and shared expectation.
If we treat language as mere data, we risk building systems that are grammatically flawless but pragmatically tone-deaf. We end up with assistants that sound polished but not relatable, chatbots that apologize too much or too little, and generative tools that echo the surface of language without capturing its soul.
But it doesn’t have to be this way.
As LLMs become more fluent, the next frontier is not raw capability, it’s appropriateness. It’s being able to speak not just correctly, but naturally. Not just in English, but in Portuguese as it is spoken in Rio, in Spanish as it lives in Bogotá, in language as people actually use it.
This is not a purely technical challenge. It is linguistic. Cultural. Human.
It calls for a new kind of engineer, one who understands that language is not a string of tokens, but a social contract. One who knows that meaning emerges from interaction, not just syntax. One who can bring together the power of machine learning with the depth of linguistic and cultural insight.
And that might be you.
If you’ve spent years studying language, not just programming it…
If you’ve paid attention to tone, variation, and voice…
If you can hear when something “feels off”, and explain why…
Then this field needs you.
Context engineering isn’t just a technical discipline. It’s the future of human-centered AI. And the people who understand the lived reality of language will be the ones best equipped to shape it.
Authored by Davi de Andrade Guides
A Staff Software Engineer and linguist-at-heart. 🗣️♥️
Working at the intersection of backend architecture, GenAI systems, and language.
With over two decades of experience in software engineering and a deep grounding in linguistic theory, he designs human-centered AI systems that go beyond token prediction, into the realm of meaning, voice, and cultural nuance.
Visit daviguides.github.io for more insights