My specialty is developing intelligent interfaces that leverage generative AI technologies to deliver exceptional end-user experiences. With expertise in both full-stack development and AI integration, I bridge the gap between cutting-edge technology and intuitive design.
Explore my recent work integrating generative AI with modern web development
Developed a full AI-powered search interface that delivers context-rich answers via an LLM with supplementary features like JSON formatting/parsing, Markup, and inline citations.
View demoCreated a research assistant chatbot that leverages Claude and Flowise for knowledge retrieval, with RAGAS evaluation for quality assurance and session orchestration.
View DemoLed development of a content-aware quiz tool using Gemini APIs, featuring prompt engineering for question generation and comprehensive analytics tracking.
View DemoCombining fullstack development skills with cutting-edge AI technologies
RAG, Claude, Gemini, LangFuse, Flowise, RAGAS
React, Vue, Tailwind, JavaScript, TypeScript
REST, GraphQL, WebSockets, Session Management
Figma, User Flows, Prototyping, Accessibility
Tracking, A/B Testing, Performance Monitoring
My journey in development and AI integration
Interested in integrating AI into your web projects? Want to discuss a potential collaboration? I'd love to hear from you.
Ask me a tech question (Gemini powered)
Type an IT-related query and get synthesized, search-style results.
Generate multiple-choice questions from an IT topic, answer them, and get a score.
Chat-style “Research Assistant” that answers IT-related questions with clean formatting, chat history, inline citations, and reference linking. (1) summarizes the current page, (2) answers user questions using only TechTarget content via RAG, (3) injects inline, linkable citations, and (4) renders a polished chat UI with rate-limits, error UX, surveys, and analytics.
Synthesized a client-side Gen-AI Search experience with a search-engine input, loading skeletons, summary with inline citations, follow-ups, important terms, definition, content recommendations, and a multi-tab results UI. Designed to plug into your RAG route and render source-aware snippets with scores and outlinks and link to your relevant content.
Topic-based multiple-choice quiz: generates questions (LLM or local fallback), lets users answer, shows correctness and explanations, and computes a final score with per-question review.