AI DATA CONTRIBUTER
Contributed to Large Language Model (LLM) training by creating, annotating, and evaluating high-quality datasets focused on computer programming and code understanding. The work involved generating structured prompt–response pairs, labeling programming-related data, and reviewing outputs to improve model accuracy and reasoning. Responsibilities included preparing datasets for supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and evaluation tasks. This included code-related question answering, function-calling scenarios, and problem-solving prompts across multiple programming contexts. The goal was to ensure clear instructions, accurate responses, and well-structured training examples that improve model performance in code generation, debugging, and explanation tasks. The project also involved quality control, guideline adherence, and iterative dataset refinement to support reliable LLM behavior in software development use cases. Work emphasized clarity,