Ratih Pramestri

Key Skills

Software

Data Annotation Tech

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Translation Localization

Prompt Response Writing SFT

Freelancer Overview

I am an Applied Linguistics (M.A.) graduate with a strong background in English language teaching, content evaluation, and discourse-based research. My expertise includes analyzing and improving instructional materials, conducting multimodal and critical discourse analysis, and providing structured, evidence-based feedback to ensure clarity and consistency. I have hands-on experience developing and revising language learning content, focusing on usability and effective communication, which translates well to data labeling and annotation tasks, especially in natural language processing and educational domains. My skills in structured writing, attention to detail, and adherence to guidelines equip me to deliver high-quality, consistent training data for AI and machine learning projects.

Labeling Experience

Multilingual AI Trainer

Data Annotation TechTextTranslation LocalizationPrompt Response Writing SFT

Contributed to large-scale LLM training and evaluation projects involving bilingual (English–Indonesian) text data. Performed translation/localization, prompt and response writing for supervised fine-tuning (SFT), and comparative evaluation of AI-generated outputs. Assessed responses for factual accuracy, coherence, instruction-following, tone, bias, safety, and pragmatic appropriateness in conversational contexts. Conducted fact-checking using reliable public sources and identified hallucinations, unsupported claims, and logical inconsistencies. Applied linguistic analysis (grammar, semantics, discourse coherence, register) to ensure high-quality, culturally appropriate outputs. Maintained strict adherence to annotation guidelines, quality control rubrics, and calibration standards in a production environment.

Contributed to large-scale LLM training and evaluation projects involving bilingual (English–Indonesian) text data. Performed translation/localization, prompt and response writing for supervised fine-tuning (SFT), and comparative evaluation of AI-generated outputs. Assessed responses for factual accuracy, coherence, instruction-following, tone, bias, safety, and pragmatic appropriateness in conversational contexts. Conducted fact-checking using reliable public sources and identified hallucinations, unsupported claims, and logical inconsistencies. Applied linguistic analysis (grammar, semantics, discourse coherence, register) to ensure high-quality, culturally appropriate outputs. Maintained strict adherence to annotation guidelines, quality control rubrics, and calibration standards in a production environment.

2025

Education

Work History