This guide explains why dataset quality directly impacts AI evaluation results and walks through demand analysis, data‑source selection, multi‑stage quality assurance, bias detection, version management, and continuous updates, illustrated with concrete examples and a hands‑on exercise.
This guide walks AI engineers and project leaders through the full lifecycle of high‑quality dataset creation—from defining requirements and setting annotation standards to data collection, preprocessing, labeling, augmentation, evaluation, and continuous iteration—providing concrete metrics, compliance rules, and tool recommendations to avoid common pitfalls.
This article examines OpenAssistant, the world’s largest open‑source ChatGPT replica, detailing its dataset of over 160 k annotated conversations, the fine‑tuned LLaMA and Pythia models, evaluation results against GPT‑3.5‑turbo, practical usage examples, and the project's current limitations and future directions.
iQIYI released iQIYI-VID, the world’s first multimodal, multi-angle celebrity video dataset (1,000 hours, 500,000 clips, 5,000 celebrities) for a new AI competition focusing on multimodal video person recognition, which has attracted global university teams and top computer‑vision judges to advance AI understanding in entertainment.
