Challenge
A well-funded AI research lab building a multilingual LLM had a fundamental alignment gap. Their RLHF data was English-only with US-centric cultural assumptions, missing 5 major Indic languages: Hindi, Bengali, Tamil, Telugu, Marathi. Production consequences: culturally inappropriate responses in Indic languages (wrong honorifics, insensitive phrasing, wrong idioms). Factually incorrect responses on Indian topics. Safety violations at 4.2% rate above acceptable thresholds for product launch. They needed a RLHF partner with genuine native-language expertise - cultural insiders, not just translators.
Solution
Component 1 RLHF Preference Ranking: 200+ trained annotators ranked model output pairs for helpfulness, harmlessness, and cultural appropriateness. Annotators were native speakers with domain expertise. All trained on the client content policy before annotation. Component 2 Safety Labeling and Red-Teaming: Systematic red-teaming to surface harmful, biased, or policy-violating outputs. Safety annotation across 8 harm categories. Culturally-specific harm categories added for Indic language contexts. Component 3 Instruction-Following Evaluation: Evaluation of model compliance with explicit user instructions. Cultural appropriateness of response tone and register. Component 4 Data Cleaning: Deduplication. PII redaction. Format standardisation to JSONL. Krippendorff Alpha maintained at 0.83 or higher throughout.
Results
Key outcomes: 28% improvement in human preference scores on Indic language responses after RLHF fine-tuning. Safety violation rate dropped from 4.2% to 0.6% across all languages. Cultural appropriateness ratings improved significantly - responses feel naturally native, not translated. The improved model enabled a successful product launch in India. eQOURSE RLHF annotation established a new internal quality benchmark for the client globally.