The data freshness challenge in artificial intelligence applications
Large language models (LLMs) are trained over extended periods using expensive compute resources, resulting in knowledge that becomes stale over time. This creates a significant gap between current information and what AI systems can access. This limitation becomes critical when organizations need AI applications that understand and respond to real-time business events, current industry conditions, or recent data changes.
You can solve this data freshness challenge by augmenting your AI applications with real-time data through change data capture (CDC) streams. Streams provide an ordered record of changes on tables, including keys, before/after images, and metadata such as TTL, with events retained for up to 24 hours. You can consume these rich change events in parallel by Amazon Kinesis Client Library (KCL) applications, which offer checkpointing, fault tolerance, and horizontal…
