Production RAG: what I learned from processing 5M+ documents
SMRTR summary
After processing over 13 million documents across two enterprise RAG systems, five key improvements proved most valuable: generating multiple parallel queries from conversation context, reranking chunks to dramatically improve relevance, developing custom chunking strategies, adding metadata to language model inputs, and routing non-RAG questions to separate APIs.
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