Government & Policy AI Prompt Auditor — हिन्दी

Auditing Government & Policy prompts in हिन्दी

Writing government & policy prompts in हिन्दी is a different discipline from writing English prompts and translating the output. Hindi prompts perform best when written in Devanagari rather than Romanised Hindi. Code-mixing (Hinglish) degrades quality on every model — pick one register and stick to it. The auditor on this page scores your prompt against four 2026 frontier models — GPT-5, Claude 4.6, Gemini 3 Ultra and DeepSeek V3.2 — and ranks them by V-Index (quality per dollar) for policy briefs, RFP responses, and parliamentary Q&A preparation.

Government & Policy work is unforgiving of model error. A hallucinated citation, a missed performance obligation, a wrong incoterm — each one costs hours or money downstream. The cheapest model is rarely the most expensive; the model that hallucinates least on your specific workload is. Our V-Index methodology measures both, in हिन्दी, against the actual prompt you intend to ship.

Four pillars of a high-V-Index government & policy prompt

1. Pillar 1

   Audience seniority

   Minister-level brief is one page, max 3 recommendations. Director-level can be 4 pages with options. State seniority explicitly.

2. Pillar 2

   Political constraint stack

   Coalition dynamics, electoral cycle, manifesto commitments. The model needs the political map to write credible advice.

3. Pillar 3

   Evidence + counter-argument

   Every policy brief needs the strongest opposing view stated and rebutted. Build that requirement into the prompt.

4. Pillar 4

   Implementation realism

   Cost, legislative vehicle, departmental owner, timeline. Without these the brief is academic.

Five mistakes that tank government & policy prompt quality

• 01Wrong audience seniority — minister gets a 12-page brief, director gets a one-pager.
• 02No political context — recommendations are politically naive.
• 03Missing counter-argument requirement — brief looks one-sided.
• 04No implementation detail — academic rather than actionable.
• 05Trusting model-cited parliamentary references without verification.

Three example government & policy prompts to audit

Each version below progressively adds the constraints discussed above. Run them through the auditor and watch the V-Index move.

Frequently asked questions

Which model is best for government & policy prompts in हिन्दी?

There is no universal answer — it depends on whether you optimise for cost, quality, or hallucination rate on your specific workload. The auditor on this page ranks GPT-5, Claude 4.6, Gemini 3 Ultra and DeepSeek V3.2 by V-Index for your exact prompt in हिन्दी. As a rule of thumb in 2026: Claude 4.6 leads on government & policy reasoning tasks, DeepSeek V3.2 wins on cost-per-quality, GPT-5 is the safest all-rounder.

Does prompt language affect output quality?

Yes — significantly. Prompts in हिन्दी route to different attention patterns than English prompts, even when the underlying request is identical. Hindi prompts perform best when written in Devanagari rather than Romanised Hindi. Code-mixing (Hinglish) degrades quality on every model — pick one register and stick to it. For high-stakes government & policy work, audit in both languages and compare.

Is the free tier enough for government & policy work?

The free tier (3 anonymous audits + 5/day signed-in) is enough to validate a prompt template you'll reuse. For daily government & policy work — refining client-specific prompts, generating PDF audit reports, switching between हिन्दी and Professional English — Pro at $99/year removes the limits.

How is V-Index calculated?

V-Index = curated quality score (1–10) ÷ input price per 1M tokens (USD). A higher V-Index means more quality per dollar. The quality score is task-weighted: a model that is excellent at reasoning but weak at extraction will score differently for a government & policy extraction prompt than for a government & policy reasoning prompt.

Are model citations reliable?

No. Every frontier model in 2026 still fabricates citations at a non-zero rate, including the most expensive ones. The mitigation is in the prompt: require the model to refuse rather than guess, and verify every citation manually before shipping. Our audit reports flag citation-heavy prompts with an explicit hallucination-risk score.