Large-Scale Enterprise RAG Chatbot System

Large-Scale Generative AI Chatbot System

Design a high-scale conversational AI system similar to ChatGPT. The system must support millions of concurrent users, provide sub-second initial responses (TTFT), and maintain high factual accuracy despite a static knowledge cutoff. Detail the end-to-end lifecycle including data cleaning of multi-terabyte crawls, model alignment using preference learning (RLHF/DPO), retrieval-augmented generation (RAG) for real-time grounding, and serving optimizations like KV-caching and continuous batching to handle high QPS on distributed GPU clusters.
TransformersDPOSFTRAGvLLMPagedAttentionBPEFlashAttentionSpeculative DecodingGQALSHFSDP
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Large-Scale Enterprise RAG Chatbot System

Design a high-scale, retrieval-augmented generation (RAG) chatbot system for an enterprise corpus of 10 million documents. The system must support 100 million DAU with sub-second response times. Detail the end-to-end ML lifecycle, specifically focusing on multi-stage retrieval strategies, latency optimization for LLM inference (quantization, caching), handling data freshness, and implementing robust evaluation frameworks (e.g., RAGAS) to mitigate hallucinations. Address the production trade-offs between model size, retrieval accuracy, and operational cost.
RAGLLMTransformersvLLMMilvusQuantizationCross-EncoderDPOSFTRedisKafkaHNSW
10
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