Legal AI Prompt Auditor — 中文

Auditing Legal prompts in 中文

Writing legal 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 contracts, charters, litigation memos and regulatory filings.

Legal 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 legal prompt

1. Pillar 1

   Jurisdiction-first framing

   Always state the governing law, court, and applicable statute up front. A prompt that begins with 'Under DIFC law…' produces a fundamentally different draft than one that opens with 'Generally speaking…' — the former forces the model to retrieve jurisdiction-specific precedent, the latter invites hallucinated case names.

2. Pillar 2

   Cite-or-decline instructions

   Add an explicit 'cite the exact section number or refuse to answer' clause. Hallucinated case citations are the #1 source of sanctions in 2026 — the prompt itself is your first line of defence.

3. Pillar 3

   Party and capacity disambiguation

   Name the parties, their capacities, and the transaction stage. 'Draft a clause' is useless; 'draft a force-majeure clause for Party A as seller in a Saudi Aramco JV, governed by DIFC law, post-signing amendment' is auditable.

4. Pillar 4

   Output format pinning

   Specify clause numbering, defined-term capitalisation, and whether you want a redline. Without this, every model picks a different convention and your paralegal spends 40 minutes on cleanup.

Five mistakes that tank legal prompt quality

• 01Asking for 'a contract' instead of a specific clause — produces a generic template instead of usable drafting.
• 02Omitting governing law — the model defaults to US/Delaware and you get the wrong indemnity standard.
• 03Forgetting to specify the audience (judge, opposing counsel, client) — tone and citation depth depend on it.
• 04Pasting the full agreement when only one section matters — wastes tokens, dilutes the model's focus.
• 05Trusting cited cases without verification — every model still fabricates citations at non-zero rates in 2026.

Three example legal 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 legal 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 legal 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 legal work, audit in both languages and compare.

Is the free tier enough for legal work?

The free tier (3 anonymous audits + 5/day signed-in) is enough to validate a prompt template you'll reuse. For daily legal 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 legal extraction prompt than for a legal 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.