Stop Answering Today's Questions with Yesterday's Data: Low-Latency RAG with Kafka and Flink

Your shiny, new, cutting-edge RAG microservice is only as smart as its context. And if that context is refreshed by a slow, batch-driven job, your AI is essentially answering today’s critical questions by consulting yesterday’s equivalent of a stale newspaper.

It’s time to transition your RAG architecture from batch dependence to streaming certainty. Let’s discuss a “streams-first” approach to building data pipelines with fresh context. We’re using Apache Kafka and Apache Flink to build the always-on knowledge backbone your RAG microservices deserve.

We’ll focus on the foundational engineering practices that guarantee reliability and access to real-time data:

* Kafka as the data substrate: Data streams based on a fault-tolerant, high-throughput source of truth to capture every critical change across your organization.* Flink’s Real-Time Prep: Leveraging Flink for stateless transformation, stateful contextual enrichment and streamlined chunking—performing the heavy lifting as data arrives.* Production-Grade Guardrails: Implementing crucial patterns like Exactly-Once Semantics (EOS) for data consistency and establishing a Dead Letter Queue (DLQ) strategy for reliable error handling.

Join this session for a discussion of the core data principles needed to build truly resilient RAG microservices where the knowledge base is always measured in seconds, not days.