AI & Deep Learning / Prompt Engineering work (PyTorch, RAG, prompt engineering, LLM lifecycle management)
Built and iterated AI prompt workflows (including structured JSON prompts and Chain-of-Thought style prompting) to support LLM behavior and downstream application integration. Documented and applied prompt engineering practices for Retrieval-Augmented Generation (RAG), focusing on how prompts guide retrieval, generation, and response quality. Performed testing-oriented iterations that align model outputs with application requirements and safety/quality goals. • Structured prompt templates using JSON schema constraints. • Applied Chain-of-Thought prompting patterns where appropriate. • Designed RAG-oriented prompt flows for web application integration. • Managed LLM lifecycle practices during experimentation.