Enterprises Optimize Serialization To Reduce Token Waste

Organizations scaling retrieval-augmented generation (RAG) and agent-driven AI into production face a performance problem: inefficient data serialization consumes 40%–70% of tokens, inflating API costs and reducing effective context windows. The article presents three optimization strategies—schema-aware formats, numerical precision control, and hierarchical flattening—and recommends a preprocessing pipeline with schema detection, compression rules, deduplication, token counting, and validation to achieve 60%–70% context reductions and lower per-query costs.