Building a Custom Hindi Glossary System with Claude: From 76% to 92% Accuracy in 10 Months

A solo developer in Bangalore built a custom glossary system for Claude to improve the accuracy of Hindi domain-specific content generation. Over 10 months, the error rate for domain vocabulary dropped from 24% to 8% (accuracy improved from 76% to 92%). The project now serves 310 customers at $10.8K MRR for Hindi customer support and blog content.

The Problem: Generic Hindi for Business Terms

Claude's default Hindi uses generic translations for business terms. For example, it outputs "bhugtan" (payment) instead of "UPI bhugtan" (UPI payment). This domain vocabulary gap caused a 24% error rate in specialized content.

The Glossary System Evolution

The developer iterated through three approaches over 10 months:

• Months 1-3: Manual Glossary (200 terms). Pasted as context with every query. Accuracy improved from 76% to 84%.
• Months 4-6: Structured Glossary with Categories (400 terms). Terms organized into tax, payment, compliance, and business types. Accuracy went from 84% to 88%.
• Months 7-10: Example-Based Glossary (600 terms). Each term includes 2-3 example sentences showing correct usage in context. Accuracy reached 92%.

Key Takeaways for Non-English AI Applications

The developer emphasizes that a glossary is not just a list—it's a teaching tool. Simply increasing term volume helped only marginally. Categorization added value, but example sentences with context provided the biggest accuracy improvement. The remaining 8% error rate is concentrated in regional variations and newly introduced regulatory terms.

For developers building non-English AI applications, this case study demonstrates that glossaries should include example sentences to teach the model context better than definitions alone.