Government & Policy AI Prompt Auditor — 中文

Auditing Government & Policy prompts in 中文

Writing government & policy prompts in 中文 is a different discipline from writing English prompts and translating the output. Simplified vs Traditional matters — state it explicitly. Qwen 3 Max leads on Mandarin; GPT-5 and Claude are close behind. Avoid mixing English technical terms unless they're industry-standard. 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 中文, 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 中文?

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 中文. 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 中文 route to different attention patterns than English prompts, even when the underlying request is identical. Simplified vs Traditional matters — state it explicitly. Qwen 3 Max leads on Mandarin; GPT-5 and Claude are close behind. Avoid mixing English technical terms unless they're industry-standard. 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 中文 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.