Hi, I’m Yongjae Kim, a frontend engineer from Seoul, Korea. I have four years of frontend experience in production SaaS and I’m currently building full-stack AI products. I enjoy turning complex problems into intuitive, scalable interfaces and systems.
I built and operate Nativ, a PDF-based AI chat tutor for language learners, with a hybrid RAG pipeline and LLM-as-Judge evaluation in production. I’m passionate about cost-efficient architectures, thorough testing, and shipping end-to-end features—from UX design to deployment.
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- Fine-tuning was never an option — every user uploads their own textbook. Designed RAG with hybrid search (semantic + keyword) so the AI always answers from context, not generic knowledge.
- Language tutoring needs consistent accuracy over reasoning depth — switched from Anthropic to OpenAI and right-sized models per task — A/B tested for quality, cost modeled at 94% reduction ($133 → $8.60/month) at scale.
- 163 backend integration tests in CI/CD, plus a cross-model LLM-as-Judge eval (GPT tutor → Claude judge) that automatically catches RAG quality regressions on every prompt change.
- Designed a text-selection popup inside the PDF viewer — translate, speak, practice pronunciation, save to vocabulary, or ask the AI tutor — so learners stay focused without ever leaving the page.
- Launched as a live freemium SaaS with subscription billing and Docker deployment on $3–8/month infrastructure.
PDF-based AI chat tutor for language learners
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