We've built a seeded RAG system that uses a lightweight knowledge cache to minimize redundant retriever calls. The LLM first tries to answer a query using cached context from previous questions; if it determines that the information is insufficient, it triggers the retriever to fetch new data and updates the cache. This approach allows semantically related or follow-up questions to be answered instantly from the existing cache, reducing latency, cost, and retrieval frequency while keeping responses contextually consistent across a conversation.
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