Design an end-to-end retrieval-augmented generation system with chunking, embeddings, vector store, retrieval, and generation choices justified for your constraints.
## CONTEXT It is 2026 and retrieval-augmented generation (RAG) is the default pattern for grounding large language models on private or domain data. Most failed RAG projects fail not at the model but at the retrieval and data layers: bad chunking, weak embeddings, no reranking, and no evaluation harness. The user…
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