claude for building a custom glossary system. the domain vocabulary gap in hindi dropped from 24% to 12% error rate. the iteration took 10 months.

solo dev. bangalore. $10.8K MRR. 310 customers. hindi customer support + blog content.

the domain vocabulary problem: claude uses generic hindi for business terms. "bhugtan" (payment) instead of "upi bhugtan" (UPI payment).

the glossary system evolution:

month 1-3: manual glossary. 200 terms. pasted as context with every query. accuracy improvement: 76% → 84%.

month 4-6: structured glossary with categories (tax, payment, compliance, business types). 400 terms. accuracy: 84% → 88%.

month 7-10: example-based glossary. each term includes 2-3 example sentences showing correct usage in context. 600 terms. accuracy: 88% → 92%.

the ai content generator output in hindi is now at 92% accuracy for domain-specific content. the 8% error rate is concentrated in regional variations and newly introduced regulatory terms.

the iteration: each accuracy improvement required a different approach. volume of terms wasnt enough. categorization helped. example sentences helped most.

for devs building non-english AI applications: the glossary isnt a list. its a teaching tool. example sentences teach the model context better than definitions.