Projects

Optimizing AI Agent

My focus includes integrating support for multiple large language models to a custom AI Agent, allowing the system to leverage different LLMs to improve response quality, flexibility, and robustness. In parallel, I am optimizing prompt engineering strategies to make interactions more efficient and less chat-heavy, while still providing clear, step-by-step guidance. This involves refining prompts, managing context more effectively, and reducing unnecessary follow-up exchanges. The project aims to enhance the overall learning experience by delivering concise, instructional, and user-friendly AI assistance.

Key Technical Highlights

• Designed and implemented a multi-LLM AI system with provider-agnostic architecture and model-specific adapters
• Applied advanced prompt engineering to enforce guided, step-by-step tutoring behavior and prevent solution leakage
• Built context-aware retrieval mechanisms to ground AI responses in assignment-specific instructional data
• Developed semantic memory and session management for assignment-scoped AI conversations
• Integrated natural language understanding with structured user inputs for adaptive tutoring workflows
• Implemented option-based clarification strategies to reduce ambiguity and improve user decision-making
• Engineered controlled AI inference pipelines with token limits, verbosity control, and response constraints
• Designed and managed relational databases for chat history, session tracking, and user interaction logs
• Enabled secure execution and analysis of user-submitted code for real-time error diagnosis
• Performed cross-model evaluation to compare LLM behavior, latency, and instructional quality
• Implemented robust error handling and fallback mechanisms to improve system reliability

Conducted iterative experimentation and evaluation to optimize AI response quality and user experience