As organizations scale Retrieval-Augmented Generation (RAG) architectures and agent-driven AI systems into production, a critical performance issue is emerging: Poor data serialization consumes 40% to 70% of available tokens through unnecessary formatting overhead. This translates to inflated API costs, reduced effective context windows and degraded model performance.
The problem often goes unnoticed during pilot phases with limited data volumes but becomes acute at scale. A single inefficiently serialized record might waste hundreds of
