The Tool Desk
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Start by defining what the model will actually do
An evaluation is only useful if its tests resemble the deployment. Before selecting prompts or benchmarks, write down the users, tasks, languages and consequences involved. A casual information assistant and a system used in healthcare, education, finance, legal work or public services do not carry the same risks.
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Specify language and input conditions
For every target language, record the scripts people use, expected dialect and register variation, and whether users may mix languages or write a language in Latin script. Include spelling variation, transliteration and code-switching when they occur in practice. A model that handles formal Hindi in Devanagari may behave differently with conversational Hindi-English, Romanized Hindi or another regional language.
Set the stakes and failure criteria
Describe what a wrong, unfair or unsafe response could do. Decide which failures are unacceptable for the intended use, who should review borderline cases and what should happen when the model is uncertain. There is no universally accepted pass threshold for accuracy, fairness or safety across Indian languages; thresholds need to reflect the task, risk and affected users.
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How to measure accuracy in each language
Use held-out examples for each language and task. Include ordinary requests as well as difficult cases that distinguish genuine understanding from pattern matching. Use native-level reviewers or relevant domain experts to create answer keys and judge open-ended answers. Translation-only or multiple-choice tests may miss cultural context and reasoning that matter to the task.
Build a representative test set
- Write prompts in the language and form users will use, including native script, transliteration and mixed-language input where relevant.
- Cover the task’s real range: routine cases, ambiguous questions, local references and difficult or high-stakes examples.
- For open-ended responses, define a rubric before testing. It should distinguish factual correctness, completeness, relevance and appropriate uncertainty.
- Keep a separate test set that was not used to tune prompts or the model. Record how examples were selected and which user groups or regions they represent.
Score results in a way readers can interpret
Report the metric and rubric, sample size, language, task and reviewer method. Break results out by language and task rather than presenting one pooled score that can conceal weak performance in a smaller language group. For open-ended tasks, use trained human review where needed; an automated grader can help with scale, but its own language and cultural limitations should be considered.
IndQA is one example of a culturally grounded evaluation design. OpenAI describes it as 2,278 questions across 12 languages and 10 cultural domains, created with 261 domain experts. Each item has a culturally grounded prompt, an English translation for auditability, grading criteria and an ideal answer; its rubric uses weighted criteria and a model-based grader. OpenAI also says the language question sets are not identical, so the scores are not direct comparisons of language ability or a language leaderboard.
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Fairness testing should reflect the people and institutions relevant to the intended use. Look for differences in answer quality, tone, assumptions, recommendations or willingness to help across identities and regions. A model may sound fluent while repeating a stereotype, applying a different standard to one group or misunderstanding local context.
Use locally grounded scenarios
Where relevant, include prompts involving caste and social justice, gender, religion, regional stereotypes and India-specific institutions. Use realistic scenarios rather than relying only on generic questions translated from English. Have reviewers familiar with the language and context assess whether a response is respectful, accurate and substantively fair.
Use counterfactual pairs carefully
Compare prompts that differ only in an identity cue, such as a name or stated region, then inspect whether the response changes in a material way. These pairs can expose disparate treatment, but a small set cannot prove broad fairness: results depend on wording, context and the range of identities represented. Treat them as diagnostic tests and investigate meaningful differences with additional examples and reviewers.
India’s Telecommunication Engineering Centre (TEC) identifies Standard TEC 57050:2023 as a standard for fairness assessment and rating of AI systems, unveiled on July 7, 2023. TEC describes the fairness assessment as voluntary. The standard is an assessment framework, not evidence that a particular model has been certified.
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How to test safety in Indian languages
Test harmful requests, benign requests that should receive help, ambiguous cases and prompts designed to evade safeguards. Run these tests in each relevant language and in the mixed-language forms users may actually use. English-only safety results do not establish that safeguards work in Hindi or other Indian languages.
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Measure both unsafe compliance and over-refusal
Record when a model provides harmful assistance, but also when it refuses a benign request or gives an unusable answer. A useful safety review asks whether the response is safe and appropriately helpful in the original language and context, not merely whether it appears acceptable in an English translation.
Probe realistic evasion and escalation
- Try transliteration, code-switching, slang and regional phrasing that could obscure harmful intent.
- Test role-play and multi-turn conversations, including requests that become more harmful over several turns.
- Include context-specific risks such as misinformation circulated in WhatsApp-style forwards when they are relevant to the deployment.
- Check whether safe alternatives or explanations remain understandable and useful in the language tested.
Inspect India Evals, a 2026 preprint, describes six evaluation areas including multilingual harmful-prompt safety, multi-turn jailbreak resistance and Digital Public Infrastructure safety. The Indian Responsible AI Benchmark includes Hinglish and code-switching, WhatsApp-forward misinformation and regional red-team prompts. These are useful test dimensions to adapt, not certifications or guarantees for a deployed model.
When the system uses speech or images, test those separately
Text performance does not stand in for speech or vision performance. Add modality-specific tests if users can speak to the system, hear generated speech or submit images.
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Evaluate recognition and generated speech separately. Include speakers with varied Indian accents and regional pronunciation, code-mixed utterances and realistic background conditions. Check transcription errors and whether spoken output is intelligible and appropriate for the intended users. The Principal Scientific Adviser’s paper discusses Svarah in connection with Indian-accent automatic speech recognition gaps and CoSHE-Eval for Hindi-English code-mixed ASR.
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Vision-language use
If the model interprets images or responds to image prompts, include combinations that test culturally sensitive content and context. The Principal Scientific Adviser’s paper discusses SangrahaTox as an example of culturally sensitive image-prompt safety evaluation. A text-only test cannot establish how the system handles such inputs.
How to compare models and keep results reproducible
For a fair comparison, give each model the same task definitions, prompt conditions, scoring rules and comparable data splits. Keep results separated by language, task and risk category. Do not treat scores from benchmarks with different samples or rubrics as if they were a single ranking.
- Freeze the test design. Document the prompt set, sample construction, scoring rubric and reviewer instructions before comparing models.
- Record the configuration. Note model name and version, evaluation date, system instructions, decoding settings and any external tools the model can use.
- Report disaggregated results. Show performance by language, script or input form, task and risk category, along with the sample size and scoring method.
- Preserve examples and review decisions. Keep a record of failures and borderline cases so another evaluator can understand how the scores were produced.
- Repeat after changes. Re-evaluate after model updates and before consequential deployment; a new version may change both capabilities and failure modes.
Inspect India Evals reports a study of five open-weight models, but its 2026 preprint findings are bounded by those models and the study’s methods. They should not be read as a universal ranking of current systems.
What published Indian-language resources can—and cannot—tell you
Published benchmarks can provide test ideas, evaluation structure and evidence about the specific models and prompts they cover. Their scores do not certify a model for your use case. The figures below describe the resources as reported by their publishers; their samples and methods differ, so the counts are not a basis for ranking the resources against one another.
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| Resource | Published scope | How to use it | Important qualification |
|---|---|---|---|
| IndQA, OpenAI (2025) | 2,278 questions; 12 languages; 10 cultural domains; 261 domain experts | Reference for culturally grounded questions, expert-authored criteria and weighted grading. | Questions differ across languages; OpenAI cautions against interpreting cross-language scores as direct comparisons. |
| Indian Responsible AI Benchmark, Responsible AI Labs | 212 adversarial and safety-critical prompts; 22 categories; 10 Indian language regions; eight Responsible AI dimensions | Reference for categories such as stereotypes and bias, caste and social justice, gender, political neutrality, India/US context confusion and regional red-teaming. | The publication year is not stated on the reviewed dataset page. Published scores apply to that benchmark and tested responses, not to universal model quality. |
| Inspect India Evals (2026 preprint) | Six benchmarks; a reported study of five open-weight models | Reference for evaluation areas including multilingual safety, jailbreak resistance and Digital Public Infrastructure. | A preprint and a bounded study; results depend on the models and methods examined. |
| TEC Standard 57050:2023 | Fairness assessment and rating standard unveiled July 7, 2023 | Reference for structuring fairness assessment. | TEC describes the assessment as voluntary; the standard does not itself show that a particular model is certified. |
Government coverage figures also need date and status context. An August 6, 2025 parliamentary reply said BharatGen models covered nine Indian languages and gave a roadmap toward all 22 scheduled languages by June 2026. The reply described the initiative as in pilot deployment and not yet publicly or institutionally released at that time. A roadmap is not confirmation that its milestone was achieved; check current official availability before relying on it.
What to publish with an evaluation
A useful report lets readers understand what was tested, where the model performed well or poorly and how far the findings can be generalized. Include:
- the intended users, tasks, supported languages, scripts and relevant input forms;
- the model name, version, evaluation date, system instructions, settings and external tools;
- how examples were sampled, who reviewed them, and the metrics or rubric used;
- results by language, task and risk category, with sample sizes and uncertainty where available;
- important failures, known gaps and the use-case-specific thresholds applied.
A single aggregate score can be convenient, but it should not replace this breakdown. The most useful conclusion is whether the evidence supports the particular deployment, for which users and conditions, and with what remaining risks.
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