Data quality drives AI performance for RAG-based search and Yi 1.5 model

AI Impact Summary

AI initiatives relying on Retrieval Augmented Generation (RAG) and large-model deployments depend on curated, task-specific data to deliver reliable results. The article emphasizes that data quality—relevance, completeness, timeliness, and bias mitigation—must be built into the ML lifecycle, otherwise systems like RAG-based search and models such as Yi 1.5 will underperform or propagate harmful biases. It also notes governance artifacts (dataset provenance, transparency, and participatory data collection) as essential controls to improve safety, privacy, and reproducibility. For technical teams, this implies prioritizing data curation, provenance tracking, and governance processes early in product development to maximize model quality and minimize risk.