Evaluating AI Understanding of Indian Languages and Culture with IndQA

Introduction: Why IndQA Matters in the World of AI

India, with its vivid tapestry of languages, customs, and traditions, presents one of the most complex cultural mosaics anywhere on the planet. As someone who regularly works with diverse teams and clients across the globe, I often find myself amazed—and occasionally frustrated—by how limited most artificial intelligence models are when it comes to understanding life beyond the English-speaking bubble. Even with the best intentions, so much gets lost in translation.

So, when I came across the news of IndQA—a fresh benchmark for assessing how AI copes with Indian languages and everyday realities—I couldn’t help but feel a genuine sense of anticipation. Through my work, I’ve witnessed firsthand how the lack of nuanced, locale-specific evaluation can leave non-English users underserved by even the smartest tools. Let me walk you through how IndQA is aiming to flip the script.

What is IndQA? Redefining AI Benchmarks for India’s Diversity

India isn’t just a land of a billion people. It’s home to **dozens of official languages**, an untold number of dialects, and an almost endless supply of cultural quirks. OpenAI’s introduction of IndQA signals a move towards evaluating AI models in a manner that’s as rich and varied as everyday Indian life.

IndQA is not just another dataset. It’s a meticulously curated testbed, crafted with the express goal of determining how well an AI truly grasps **not only the linguistic aspects**, but also the cultural, social, and practical realities faced daily by millions in India.

• Purpose: To ensure AI models are genuinely usable in Indian contexts
• Languages: 12, including Hindi, Bengali, Marathi, Tamil, Telugu, Punjabi, Odia, Malayalam, English, and even the rapidly popularising 'Hinglish’
• Questions: 2,278 authentic, context-rich queries, originally created in the local languages
• Experts: Contributions from 261 Indian journalists, linguists, scholars, and professionals

It’s not some theoretical exercise either. The practical approach here is palpable—from the coverage of cricket and cuisine to questions about spirituality, politics, and street-level life, IndQA appears determined to leave no stone unturned.

I’ve long believed that benchmarks only have value if they honestly reflect the audience they’re meant to serve. For too long, the tech industry has treated the non-English-speaking world as an afterthought. IndQA, thankfully, sets out to change that.

How Was IndQA Created? The Power of Local Expertise

If you’ve ever tried translating a joke or an idiom from one language to another, you know the struggle. It’s not just about swapping out words; there are layers of nuance, rhythm, and cultural baggage.

IndQA recognises this reality by prioritising genuine authenticity. Every single question was first imagined, drafted, and shaped in its source language—no crude translations, no cutting corners.

Who Built IndQA?

OpenAI assembled a broad coalition of local experts: journalists in Mumbai, linguists in Bengaluru, academics in Chennai, and cultural commentators from every walk of life. As someone who’s spent plenty of time trying to explain local context to AI-driven systems, I found this bit especially heartening.

• Team of 261 seasoned professionals
• Cross-disciplinary input: art, cuisine, sport, literature, religion, daily routines, law, and much more
• Collaborative review and curation—every item went through layers of scrutiny

What Makes IndQA Questions Distinct?

Instead of rehashing textbook trivia, the questions in IndQA are rife with local colour and idiomatic flair. The very first time I scrolled through their sample set, I couldn’t help but grin—here were puns, tongue-in-cheek references, and context clues only someone steeped in the day-to-day realities of Indian life could possibly catch.

• Original questions in each language, preserving idioms, slang, jokes, and subtlety
• Extensive thematic breadth: from classic works of literature to popular street foods, iconic film moments to celebrated sports trivia
• Careful balance between knowledge recall and real-world comprehension

It’s a far cry from the generic, surface-level tests I’ve grown used to. This makes all the difference—AI doesn’t get to skate by with generic answers anymore.

The IndQA Evaluation Method: Not Just About Getting It Right

It’s tempting to think of “AI scoring” as a matter of getting the correct answer and collecting the points. Traditional benchmarks have, for years, focused on multiple-choice exercises, spelling quizzes, or simple translations.

IndQA flips that model. Their scoring rubrics look for evidence of *true* understanding—attention to cultural context, accurate reference to local customs, fine-grained language subtleties, and the ability to answer in a manner that makes sense to the ordinary Indian user.

Rubric-Based Assessment: Moving Beyond Fill-in-the-Blanks

Each AI answer doesn’t just get pass/fail marks. Instead, the response gets picked apart by a set of specialist-designed criteria, covering every plausible angle:

• Does the answer truly address the question?
• Does it reference the correct cultural, historical, or social context?
• Is the use of language accurate, idiomatic, and sensitive to local expectations?
• Does the response demonstrate an understanding of nuance and subtext?

I’ve personally seen too many AIs stumble when it comes to reading between the lines. IndQA, with its layered evaluation, isn’t letting models off easy. This is, without question, a far more ambitious yardstick than anything that’s come before.

Adversarial Testing: Separating the Wheat from the Chaff

To avoid giving AI the chance to “game the test,” the IndQA team subjected their questions to the most advanced models before finalising the item set. Only those questions that routinely tripped up the best-performing systems made the final cut.

It’s something like seeing a chess prodigy taken down not by grandmasters, but by tricksters and hustlers in the park—only, in this case, the challenges are crafted by people who know precisely how local knowledge works. From my own experience trying to “outsmart” AI models with corner-case scenarios, I recognise the necessity—and the cleverness—of such an approach.

Why IndQA Changes the Game for AI in India

When working on AI implementations in sales and marketing, I’ve often run into the uncomfortable truth that technology companies treat non-English-language markets as second class. There’s a lot of talk about localisation, but actual *local intelligence* remains elusive.

Here’s where IndQA makes a critical difference:

• Shifts the focus from generic accuracy to genuine local usability
• Raises the standard for what it means to “know” Indian languages and culture
• Puts pressure on AI models to transcend token translation and engage with humans as they are, where they are

It’s refreshing, honestly. I remember countless meetings where marketing teams assumed that, say, transliterating a campaign slogan into Hindi would “do the trick.” IndQA is a bit of a reality check—real engagement needs real understanding.

The Scale of the Challenge: Serving the Subcontinent

India’s population isn’t just large—it’s mostly non-English-speaking. Over a billion people interact primarily in their mother tongues, *not* in the language of international business.

Key facts that drive this home:

• India is the second-largest user base for AI-powered chat platforms, yet the majority seeks interaction in languages other than English
• With so many official languages and regional subcultures, simplistic approaches simply don’t cut it
• The diversity on display in India can quickly reveal any model’s weaknesses in cultural and linguistic understanding

It’s no exaggeration to say that an AI excelling on IndQA is more likely to “make sense” for ordinary Indians—those who may never type a sentence of English in their lives.

Cultural Nuance: Preserving Authenticity in Indian Contexts

As someone who’s always got one foot in language and the other in tech, I don’t need to be convinced about the importance of local flavour. IndQA has made a deliberate—and smart—choice to keep original questions and answers in their languages of origin.

Why does this matter so much?

• Nuance: Language isn’t just vocabulary. It’s attitude, politeness, liveliness.
• Humour: If a pun or joke doesn’t land, or worse, gets garbled, the magic’s gone.
• Context: Certain sayings, taboos, or customs only make sense to those who’ve lived the reality.

From my perspective, skipping translation and sticking to the source language is the only way to capture these textures. The alternative? Bland, vanilla AI that might be technically accurate but deeply unhelpful—or, at times, bizarrely out of touch.

IndQA’s Scope: Topics Covered and Their Relevance

One of the things I most appreciate about how IndQA is structured is its sheer breadth. This isn’t some narrow, academic exercise. Instead, you’ll find questions on:

• Culture & Arts: Music, festivals, folk traditions, cinema—stuff that shapes the national character
• Cuisine: Regional specialties, food rituals, dietary customs—curry is just the tip of the iceberg
• Literature & Linguistics: Famous authors, classic works, idiomatic usage, historical scripts
• History: Moments and movements that still shape political and social discourse
• Religion & Spirituality: Diverse beliefs, practices, and holy sites
• Sport: Cricket may be king, but there’s plenty else to explore
• Everyday Life: Street experience, shopping, family traditions, local business practices
• Media & Entertainment: TV tropes, social influencers, meme culture
• Law & Ethics: Legal systems, moral dilemmas, current affairs
• Architecture & Design: From Mughal palaces to modern skyscrapers

It brings to mind the old saying: the devil’s in the details. AI models, if they hope to “fit in,” need to swim in exactly these waters.

The Rubric: How Experts Evaluate AI Responses

If you’ve ever graded essays—whether as a teacher or as a punter in a pub quiz—you’ll know that giving fair marks isn’t just about “correctness.” It’s about fluency, clarity, and appropriateness.

IndQA’s rubric methodology mirrors this human approach.

• Each response gets marked against detailed, expert-crafted benchmarks
• Scoring considers factual correctness, clarity, idiom, tone, and relevance
• Peer review ensures blind spots or biases are minimised—no loose ends

It reminds me of university marking schemes, only with the extra pressure that your markers actually live and breathe the context every day.

Addressing the “Between the Lines” Challenge

An ongoing frustration of mine with AI (and automated translations, in particular) is that they frequently miss the implied meaning. They may string the right words together, but the outcome just… doesn’t feel right.

IndQA refuses to settle for surface-level results. AI must demonstrate that it “gets” the subtext—a sniff of irony, a local metaphor, a phrase that only makes sense in Delhi or Chennai. That’s precisely what genuine understanding is all about, and honestly, that’s why customers remember great conversations.

Adversarial Filters: Stress-Testing AI

Even if you construct a robust set of questions, it’s all too easy for well-trained models to find ways to “cheat”—copying the patterns, but missing the essence.

The IndQA process, as I understand it, involved putting every draft question through the wringer of top-tier AI models. If a question could be aced by a highly generalised model trained on internet data, it simply didn’t make the cut.

• Toughest questions only: Only the items that tripped up AI survived
• Avoiding “test coaching”: Designed to minimise gaming of the system by AI trained to pass standard exams
• Continuous refinement: Periodic review and update to keep pace with evolving AI capabilities

This isn’t just smart test design—it helps keep human-AI interaction honest and, dare I say, genuinely valuable.

Practical Benefits: How IndQA Impacts Indian Users and Businesses

If you’ve been around tech launches in India, you’re probably familiar with the disappointment when the “localisation” consists mostly of a hastily translated home screen. IndQA, by contrast, is set up to make AI work for real people, in real settings.

Consequences for Indian users:

• Better AI assistants: Whether navigating government forms or seeking health advice, answers come in familiar language
• Improved access: Digital services don’t stick to “elite” circles—everyday citizens see value
• Fewer errors: By grasping genuine context and intent, mistakes and misunderstandings are reduced

And from a business perspective—especially in marketing, support, and automation, which is my own patch—the ability to talk *with* people, rather than *at* them, leads to higher engagement and retention.

Raising the Bar for Marketing and Customer Support

My own work frequently involves deploying AI for lead qualification, campaign personalisation, and customer support workflows in India. Even the slickest tools flounder if they can’t parse a paragraph of conversational Hindi or interpret a thrown-off Bollywood reference.

Thanks to IndQA, there’s now a bar to clear—a signal to AI vendors that mere translation won’t cut it. Instead, success will rely on delighting users by making them feel genuinely seen and understood. That’s something KPIs and dashboards rarely measure, but any experienced marketer or salesperson knows its worth.

The Paradigm Shift: From English-Centric to User-Centric AI

There’s a silent irony that most “global” innovations are still built, tested, and fine-tuned in English. Yet, as IndQA highlights, eighty percent of humanity prefers another tongue. For a country as polyglot as India, the old English-first model is wearing thin.

• IndQA signals the start of a shift: AI benchmarks built for users as they actually are
• Non-English communities get a seat at the tech table

As someone who’s spent sleepless nights on product localisation, that’s music to my ears.

Wider Implications: IndQA as a Model for Other Regions

While IndQA is designed for India, the blueprint goes far wider. OpenAI’s announcement makes it clear—this is just the beginning. There’s already talk of custom benchmarks for other major, underrepresented regions.

The logic is unassailable. If you can do justice to the complexity of Indian life, you’re well on your way to tackling Malay, Yoruba, Swahili, Turkish, and scores more.

The potential impact:

• Reduced AI bias towards Anglo-American culture
• Higher-level localisation for tools and services worldwide
• Inclusion of global voices in AI development, evaluation, and deployment

In my own view, this is more than just technical progress—it’s a necessary step towards fairness and relevance. After all, tools that only work “properly” for a London office or a Silicon Valley meeting room don’t really live up to their promise.

Reflections from the Frontline: My Take on IndQA’s Value

I’ve lived through the frustrations caused by poorly localised tech, and I know the satisfaction that comes when a digital assistant or chatbot finally “gets it.” For long stretches, India—and, frankly, much of the non-Western world—has been an afterthought in the AI race.

IndQA’s introduction is a measurable, concrete sign that things are changing. For someone deeply invested in both automation and cross-cultural communication, this isn’t just overdue; it’s absolutely essential.

What I Hope to See Next

• Expansion to more languages, including dialectal variations and urban/rural differences
• Integration with major natural language platforms, raising the global standard
• Regular, transparent reporting—let’s see how AIs improve over time, not just once and done

If you’ve ever found your AI “ally” embarrassingly tone-deaf, you’ll know why these ambitions matter.

Navigating AI Development: The Role of IndQA in Training and Testing

On the technical side, IndQA promises a boost at every stage:

• Training: By including richly detailed, culturally-rooted data, AI models learn more robust skills
• Testing: Developers can root out blind spots long before deployment
• Iteration: Feedback from real users closes the loop, ensuring continuous improvement

I’ve sat through enough “model review” meetings to know this isn’t just box-ticking; it’s the secret sauce of world-class performance. For enterprise teams investing in AI solutions—whether for customer care, marketing, or automation—the benefits are obvious.

Challenges Ahead: The Limits of Benchmarking

It’d be naïve to claim IndQA solves every issue overnight. There are still major hurdles in staying up to speed with language drift, cultural change, and region-specific slang. But through cycle after cycle, benchmarks like IndQA keep all the stakeholders honest.

Potential pitfalls to watch out for:

• Keeping pace: India’s cultural and linguistic landscape evolves fast; benchmarks must keep up
• Representation: Ensuring urban, rural, minority, and marginal voices don’t slip through the cracks
• Crowdsourcing: While experts are crucial, user feedback from everyday people plays a vital role

My hope is that future iterations of IndQA lean into this challenge—remaining open to on-the-ground, lived reality, even as the underlying technology races forward.

Bringing It All Together: IndQA as a Catalyst for Inclusive AI

In the swirling world of AI innovation, hard benchmarks are rare—especially ones that centre those who have long stood on the sidelines. IndQA, with its robust methodology, expert contributions, and relentless focus on authenticity, finally offers a standard worthy of India’s complexity.

For those of us who build, advise, and deploy AI solutions for businesses eager to tap into the energy and variety of Indian society, this benchmark is nothing short of a gift. It’s the yardstick by which promises of localisation will be measured—and, one hopes, delivered.

Final Thoughts

Looking back over my years navigating the twists and turns of marketing, automation, and technology adoption in Asia, I can say with conviction that IndQA is precisely the sort of tool we all need. Not an English-centric afterthought, but a resource built from the ground up with local insight and expertise.

I expect—and I hope—you’ll see its ripples soon enough: sharper ads in Marathi, more intuitive chatbots in Malayalam, smarter business tools that don’t need you to “think Western” just to get by. If there’s a phrase that sums up my experience, it’s that real progress comes not by making people change for technology, but by making technology change for people.

And, at long last, that’s the future IndQA helps to usher in.