Can a Semantic Cache Become Your Primary Retrieval Layer?

Short summary

The article proposes placing a semantic cache layer in front of RAG pipelines to avoid paying for vector search and LLM generation on repeated questions. Using a banking chatbot example with 120K daily chats and 70% FAQ repetition, it argues that caching by meaning (via embedding similarity) rather than exact string match could dramatically reduce both latency and cost. The author hypothesizes that over time the semantic cache becomes the primary retrieval layer, with RAG handling only cache misses.

• •Semantic cache embeds incoming questions and matches against previously answered ones by vector similarity, skipping vector search and LLM calls for repeats
• •A banking chatbot example shows 70% of 120K daily chats could be served from cache in 40ms instead of 2-4s full RAG pipeline
• •Hypothesis: as cache hit rate grows, RAG shifts from default path to handling only cache misses and novel questions

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