Auditing Technology prompts in বাংলা
Writing technology prompts in বাংলা is a different discipline from writing English prompts and translating the output. Bangla model quality has improved sharply in 2026 but still lags English by ~15% on reasoning benchmarks. For high-stakes work, draft in Bangla and audit the output in English. 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 বাংলা, 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 বাংলা?
- 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 বাংলা. 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 বাংলা route to different attention patterns than English prompts, even when the underlying request is identical. Bangla model quality has improved sharply in 2026 but still lags English by ~15% on reasoning benchmarks. For high-stakes work, draft in Bangla and audit the output in English. 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 বাংলা 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.