For enterprise AI applications, a simple "generated at" timestamp is insufficient to guarantee data freshness. Instead, a comprehensive freshness contract should define a chain of timestamps including event time, ingestion time, source snapshot, query time, and answer time. This contract should also specify policies for cache age, late arrivals, and exceeding thresholds. The AI's output must include trusted evidence from the connector, such as source watermarks and metadata, rather than model-invented timestamps, to ensure reliable and up-to-date answers. AI
IMPACT Highlights the need for robust data management and validation in enterprise AI deployments to ensure trustworthy and timely responses.
RANK_REASON Article discusses best practices for implementing AI in enterprise applications, focusing on data freshness and reliability.
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