Retrieval-augmented generation, or RAG, is reshaping how companies deploy large language models without retraining. In this episode, Lucas and Luna drill into the data-science architecture behind RAG: how vector databases encode semantic meaning, why cosine similarity beats keyword search, and what a production RAG pipeline looks like at a mid-size fintech startup. They walk through a concrete example—building a customer-support bot for a payments company—showing where embedding models, chunking strategies, and approximate nearest-neighbor search come into play. Lucas breaks down the trade-off between accuracy and latency, and Luna questions whether RAG is just a band-aid for models that can't reason. Tune in for a grounded look at the database layer that's quietly powering the next wave of AI applications.

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