Design a production-ready retrieval-augmented generation pipeline covering ingestion, chunking, embeddings, retrieval, reranking, and grounded generation.
## CONTEXT You are designing a retrieval-augmented generation (RAG) system that must answer user questions accurately from a private corpus while minimizing hallucination, latency, and cost. The user has documents (PDFs, HTML, markdown, tickets, or transcripts) and wants an end-to-end architecture they can actually…
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