When to use it
When a model reads well in English but its outputs in rare, low-resource, and culturally distinct languages need native judgment on accuracy, safety, and tone before release.
Prompt Evaluation service
Prompt Evaluation Services for work where a model reads well in English but its outputs in rare, low-resource, and culturally distinct languages need native judgment on accuracy, safety, and tone before release.
LLM prompt creation, model output evaluation, and RLHF-style human feedback with native-speaker judgment across 300+ languages and 4,500+ dialects: rating, ranking, and reviewing responses against a written rubric.
confidential evaluation records show a written rubric, independent raters, and senior adjudication on disputed scores across multiple languages, including pairs few vendors can staff with qualified native raters.
110,000+
verified language specialists
Language specialist network
300+
languages across active service lines
4,500+
dialects and regional variants
110+
rare and indigenous language pairs
1,000+
projects delivered since 2015
Scope dossier
Prompt Evaluation service fit confidential evaluation records show a written rubric, independent raters, and senior adjudication on disputed scores across multiple languages, including pairs few vendors can staff with qualified native raters.- Typical inputs
- Model outputs or prompt-response pairs, a scoring rubric, rating scale, safety policy, target languages, gold examples
- Controls
- Rubric calibration, independent raters, IAA on a pilot, disagreement adjudication, senior escalation
- Best fit
- Prompt evaluation, LLM evaluation, RLHF data, response rating and ranking, multilingual safety and toxicity review
Service signal
Pick the service by the result at risk.
Buyers can see the result, review depth, and file-shape fit before they compare vendors line by line.
Strongest fit
Prompt evaluation, LLM evaluation, RLHF data, response rating and ranking, multilingual safety and toxicity review
How the work runs
Rubric calibration round, then scored batches with rater agreement tracked across the run
Who this is for
Each stakeholder sees their risk.
Buyers need to see when the service fits, what can go wrong, and how review reduces rework.
VP Data Ops
Needs language coverage, throughput, and quality controls for multilingual data.
LSP vendor manager
Needs rare-language capacity without exposing the end client.
Media localization lead
Needs subtitle, dubbing, metadata, and QA workflows to meet a release date.
Specification
Lock the details that decide quality.
Use this table to compare inputs, review model, fit, and output before a buying committee asks.
| Typical inputs | Model outputs or prompt-response pairs, a scoring rubric, rating scale, safety policy, target languages, gold examples |
|---|---|
| Review path | Rubric calibration, independent raters, IAA on a pilot, disagreement adjudication, senior escalation |
| Strongest fit | Prompt evaluation, LLM evaluation, RLHF data, response rating and ranking, multilingual safety and toxicity review |
| How the work runs | Rubric calibration round, then scored batches with rater agreement tracked across the run |
Quality method
Quality starts before the first batch moves.
MoniSa uses a three-layer system: pre-production gates, in-production controls, and post-delivery review.
Screen
Profile review, nativity verification, domain questionnaire, screening call, sample task.
Calibrate
Every assigned team works against the same calibration items before production volume starts.
Pilot
The first batch is reviewed deeply so instruction drift is caught before scale.
Review
Sampling, senior review, agreement checks, and same-day feedback loops run during production.
Escalate
Critical errors trigger pause, recalibration, replacement, or operations-lead escalation.
Learn
Client feedback feeds back into resource profiles, glossary rules, and the next batch.
case evidence
Proof that matches prompt evaluation services, not generic language work.
The records below stay close to this delivery model so the proof feels operational, not decorative.
Media and metadataThree-year streaming subtitling and QC held to one bar, client details confidential.
Streaming subtitling and QC
The challenge. A streaming platform needed continuous Tamil and Hindi subtitling and QC across a growing catalog.
What we did. MoniSa ran subtitling and a separate QC lane white-label with reviewer continuity and a fixed bar.
The result. The platform received 3,100+ minutes subtitled and 2,000+ episodes QC over three years.
AI evaluationFifty languages evaluated in a compressed sprint with reviewed quality, client details confidential.
LLM fine-tuning evaluation
Problem. A model team needed 20,000 prompts evaluated across 50 languages under a compressed decision window for a fine-tuning decision.
Action. MoniSa sourced five pre-calibrated evaluators per language across all 50 tracks in parallel.
Result. The team received ~20,000 evaluations across 50 languages during the compressed sprint with reviewed quality.
Translation and LSP supportA quarter-million words of legal Khmer, terminology held exact, client details confidential.
Legal translation into Khmer
Problem. A global marketplace needed 250,000 words of legal content translated into Khmer for market entry.
Action. MoniSa sourced legal-literate Khmer linguists with a separate review pass and terminology control.
Result. The marketplace received 250,000 words of legal Khmer translation and review.
Media and metadataDevice-aware subtitle QC across five screens with reviewed quality, client details confidential.
Multi-device subtitle QC
Problem. A media catalog needed subtitle QC verified across five device types and four languages.
Action. MoniSa ran QC against a per-device checklist with native reviewers per language.
Result. The catalog received 500+ hours of subtitle QC with reviewed quality across Mac, Windows, mobile, iPad, and OTT.
Buyer questions
Ask the questions weak vendors avoid.
Short answers for buyers checking fit, coverage, quality method, and next-step readiness.
What is prompt evaluation?
Prompt evaluation is human review of how a language model responds: rating answers for accuracy, helpfulness, safety, and tone, ranking competing responses, and flagging failures against a written rubric. MoniSa runs this with native speakers when the outputs are multilingual, since quality judgments differ by language and culture.
What is the difference between prompt evaluation and RLHF data?
Prompt evaluation scores or ranks model outputs against a rubric. RLHF data is the human preference signal, which response is better and why, collected in a structured form a training pipeline can use. MoniSa produces both: rubric-based scoring and preference-style comparisons, with the same calibration discipline.
How does MoniSa keep LLM evaluation consistent between raters?
Each evaluation starts with a calibration round on shared examples. Raters score independently, inter-annotator agreement (IAA) is measured on a pilot, disagreements are adjudicated by a senior reviewer, and the rubric is tightened where raters diverge before the full run proceeds.
Can MoniSa evaluate model outputs in multiple languages?
Yes. Evaluation, prompt creation, and safety review run across 300+ languages and 4,500+ dialects with native-speaker raters. Multilingual safety and toxicity review is scoped to the policy, the languages, and the rater availability for each pair before work begins.
Next step
Send the details that decide the quote.
A useful brief names the language, content, deadline, review depth, and proof the buying team needs.
Production-ready brief
01Language pair, dialect, and script02Content or data type03Volume and deadline04QA and reviewer requirement05Security and access requirement06Proof needed for buyer approval