Start by clarifying requirements and constraints, then outline a modular agent-based RAG architecture with components like orchestrator, retrievers, and tools. Discuss optimization strategies across retrieval, generation, and system levels, emphasizing trade-offs and NVIDIA-specific accelerations.
Pro tip: Highlight how NVIDIA's hardware and software stack (e.g., TensorRT-LLM, Triton, cuVS) can accelerate each component, and quantify performance gains where possible to show depth.
Ask about use case, data volume, latency, accuracy, and cost constraints to tailor the design. This ensures the solution aligns with business and technical needs.
Define agents (e.g., planner, retriever, generator, critic) and their interactions. Specify components like vector database, LLM, and orchestration layer, and how they communicate.
List optimizations for retrieval (e.g., hybrid search, reranking), generation (e.g., quantization, speculative decoding), and system (e.g., caching, batching). Explain how each improves performance.
Map optimizations to NVIDIA tools: cuVS for vector search, TensorRT-LLM for inference, Triton for serving, and NIM for deployment. Emphasize GPU acceleration and end-to-end integration.
Compare trade-offs (e.g., latency vs. accuracy, cost vs. scalability) and propose metrics (e.g., recall@k, latency, throughput) to evaluate the system. Mention iterative refinement.
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