How to evaluate AI in Indian languages beyond a demo

A fluent demo can hide weak coverage

An AI product may sound impressive in a short Hindi or English demonstration and still fail on the language patterns people use in daily life. India has many languages, dialects, scripts and local conventions, and users frequently mix languages within a sentence. A model can produce grammatical text while misunderstanding intent, names, negation or a regional term. Evaluation should therefore begin with the intended users and tasks, not a generic claim that the model supports a language. Define whether the product must understand, generate, translate, transcribe, classify or retrieve information, because each task needs different evidence.

Build a test set from real use cases

Collect representative examples from the contexts in which the product will operate: customer support, public information, education, commerce or internal workplace tools. Include common requests and rare but consequential cases. Do not simply translate an English benchmark and assume it captures local phrasing. Work with fluent speakers who understand the task, and document how examples were collected, consented to and reviewed. Remove personal information unless it is genuinely required and properly authorised. Keep the test set separate from prompt tuning where possible, so results provide an independent check rather than measuring how well the system memorised its own examples.

Include code-switching and mixed scripts

Many users combine English technical terms with Hindi, Telugu, Tamil, Bengali or another language, and may write local language using Latin characters. They may switch scripts mid-sentence, use abbreviations, omit diacritics or spell words phonetically. Test these forms explicitly. A system that works only with carefully typed standard script may fail for mobile-first users. Include speech transcription errors, transliterated names and regional expressions where relevant. Score whether the system understood the intent and preserved critical details, not just whether its response resembles a reference sentence. There may be several acceptable answers, so evaluation needs human judgement and clear criteria.

Measure task success, not only translation quality

A translation score can be useful for a translation product, but it does not establish that a customer-support assistant answered correctly or that a form-filling system captured the right fields. Define task-specific measures: intent recognition, key fact preservation, entity accuracy, retrieval relevance, successful completion and rate of unsupported claims. For speech systems, measure transcription quality separately from downstream task success. For summarisation, check whether dates, amounts, names and important qualifications survive. A system may produce natural-sounding language that changes the meaning, so fluency should not be the only metric shown to decision-makers.

Use fluent reviewers and a consistent rubric

Human evaluation is essential when outputs depend on cultural context, idioms, politeness or regional meaning. Recruit reviewers who are fluent in the language variety and understand the use case. Give them a rubric that separates factual correctness, completeness, naturalness, respectfulness, safety and preservation of important details. Ask reviewers to mark the type and severity of each error rather than provide only a thumbs-up. Double-review a subset of examples to identify disagreement and refine the rubric. Pay reviewers fairly and avoid treating one person's preference as universal for a language community. Document reviewer backgrounds in aggregate without exposing unnecessary personal information.

Test for uneven performance across groups

Average accuracy can hide a system that performs well for one language or region and poorly for another. Break down results by supported language, script, code-switching pattern, input modality and relevant use context. Be careful with small samples: report uncertainty and avoid claims that a model is equally capable everywhere. Where the product serves users with different literacy levels or accessibility needs, test how those differences affect task completion. If some groups receive materially worse results, narrow the supported scope, improve the data and product design, or provide a human route. Do not market broad language coverage based only on a provider's language list.

Check names, numbers and high-impact details

Errors in names, addresses, dates, monetary amounts, dosage instructions or eligibility criteria can be more serious than awkward phrasing. Build targeted tests for these fields and check whether the system preserves digits, units, negation and uncertainty. In transliteration, a name may have several plausible spellings; the product should ask for clarification when the distinction matters. For high-impact workflows, use deterministic validation where possible and require human review for consequential decisions. Do not rely on a fluent answer to verify itself. Keep the original input available to authorised reviewers so they can compare the generated result against the source.

Evaluate speech and noisy environments separately

Speech recognition performance depends on accents, background noise, microphones, speaking speed and the mix of languages in an utterance. A clean studio recording is not representative of a bus stop, shop floor or phone call. Evaluate audio quality and downstream understanding separately, and include interruptions, pauses, numbers, proper nouns and common local terms. If the system fails to recognise a phrase, it should ask for repetition or offer text input rather than inventing a transcript. Provide a way for users to correct recognition errors. Collecting real audio requires special care because voices can identify people; define access, retention and deletion before gathering samples.

Protect the people whose language data you use

Language datasets can include private conversations, personal names and sensitive social context. Record the source, permissions, allowed uses and retention of each dataset. Do not scrape private communities or repurpose user submissions without considering the applicable rights and expectations. Remove identifiers where feasible, control access and ensure vendors receive only data needed for the evaluation. Synthetic examples can expand coverage but may miss real expressions or reproduce stereotypes, so validate them with speakers. If a benchmark contains offensive or sensitive material, limit access and provide context for reviewers. Good language coverage should not depend on careless collection of people's communications.

Test safety and uncertainty in each language

Safety behaviour can change across languages. A refusal or warning that works in English may be unclear, overly harsh or absent in another language. Test whether the system handles requests for harmful assistance, privacy violations and sensitive topics consistently with the product's intended policy. Also test ordinary benign questions to detect over-refusal. For factual products, include questions where the answer is unknown or sources are missing, and check whether the system can say it does not know. Translate policy instructions carefully and have fluent reviewers inspect real outputs. Do not assume that a single English safety benchmark proves equivalent behaviour across all supported languages.

Set a launch threshold and maintain it

Before launch, decide what level of performance is acceptable for each task and which errors require escalation. A single overall score is rarely sufficient. Keep a versioned evaluation set, record model and prompt changes, and rerun tests when providers update systems or the product expands to a new region. Monitor user corrections, complaint categories and failed task completion after launch, with appropriate privacy safeguards. Do not silently expand a product to a language just because a model can generate text in it. Publish supported scope honestly, identify limitations and give users a way to report failures. Language quality is an ongoing operational responsibility, not a checkbox completed at launch.