Auditing Technology prompts in 中文
Writing technology 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 system designs, API specs, and architecture decision records.
Technology 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 technology prompt
- Pillar 1
Constraint envelope first
Latency budget, throughput target, cost ceiling, team size. Architecture is constraint satisfaction; without constraints you get a generic diagram.
- Pillar 2
Failure mode enumeration
Ask the model to list 5 failure modes and how the design handles each. This is what separates a senior diagram from a junior one.
- Pillar 3
ADR format
Context → Decision → Consequences → Alternatives Considered. Pin the format and your decision log stays consistent.
- Pillar 4
Buy vs build trade-off
Always force the model to evaluate the buy-vs-build option and quantify the trade-off. Otherwise it defaults to building everything.
Five mistakes that tank technology prompt quality
- 01No constraint envelope — model proposes Kubernetes for a 100-user app.
- 02Missing team size — model assumes you have an SRE team you don't have.
- 03No failure-mode requirement — design looks robust until it ships.
- 04Skipping ADR format — decision log becomes inconsistent across the team.
- 05Letting the model default to AWS — vendor lock-in by accident.
Three example technology prompts to audit
Each version below progressively adds the constraints discussed above. Run them through the auditor and watch the V-Index move.
Write an architecture decision record for migrating a monolith to event-driven microservices.
Write an architecture decision record for migrating a monolith to event-driven microservices, and end with a one-sentence summary for the partner.
Write an architecture decision record for migrating a monolith to event-driven microservices. List every assumption explicitly. Refuse to answer any sub-question you cannot support with a cited source.
Frequently asked questions
- Which model is best for technology 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 technology 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 technology work, audit in both languages and compare.
- Is the free tier enough for technology work?
- The free tier (3 anonymous audits + 5/day signed-in) is enough to validate a prompt template you'll reuse. For daily technology 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 technology extraction prompt than for a technology 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.