When the RAG-Time Never Stops

Most RAG systems still behave as if the world moves in polite little batches. Crawl the corpus, chunk the documents, vectorise the lot, load the store, and hope nobody asks a question before the next refresh. That may work for static PDFs. It does not work when the facts are changing under your feet.

This talk is about treating retrieval as a live data problem. Or, to put it another way: if the RAG-time never stops, your knowledge system needs to keep playing.

Modern AI systems need continuous ingest, continuous vectorisation, and continuous recall over both hot and cold data. New documents, operational events, user prompts, market signals, policy updates, and application changes all need to enter the system at the right speed. Some data belongs on the fast path. Some belongs in the archive. Some needs re-ranking, enrichment, graph context, or direct lookup before it is useful. The trick is not to make everything instant. The trick is knowing what must be fresh, what must be trusted, and where it came from.

We will look at how to build multi-speed RAG pipelines using streaming platforms, stream processing, vector stores, search systems, graph databases, and event-driven enrichment. The focus is not one magic store or one “easy winner” architecture. It is how to keep recall adaptable while preserving data lineage, freshness, and operational control.

Because stale recall is not just a quality issue. In financial services, logistics, risk, news, and operational AI, stale recall can mean wrong decisions made confidently. And once the system starts making decisions from yesterday’s tune sheet, even The Entertainer cannot save you.

This is RAG as feature engineering: live, traceable, adaptive, and always in time.

Listeners will learn:

• how to design RAG systems as continuous data products rather than batch indexing jobs.
• how to separate hot, warm, and cold retrieval paths by freshness, cost, and trust.
• why lineage matters for AI recall: what was known, when it was known, and which answer depended on it.
• practical patterns for building adaptive retrieval pipelines that keep pace with changing data.