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- Design a Conversational AIBuild a production conversational AI system (think ChatGPT) step by step. See how the request path splits an inference gateway from the model servers, how the context window is assembled and token-budgeted, how conversation memory is stored and recalled, how tokens stream back over a persistent connection, and how guardrails gate every prompt and response — through an interactive diagram that grows with each concept.Read →
- Design a RAG PipelineBuild a retrieval-augmented generation pipeline step by step. See how documents are chunked and embedded, how a vector store answers semantic search, how two-stage retrieval with reranking finds the best passages, how the prompt is grounded to stop hallucination, and how evals keep a quietly-drifting index honest — through an interactive diagram that grows with each concept.Read →
- Design an LLM Inference ServerBuild an LLM inference serving system step by step. See how a request queue absorbs spiky traffic, how the prefill/decode split and continuous batching keep GPUs full, how the KV cache and paged attention make each token cheap, how tensor sharding fits a giant model, and how autoscaling rides demand — all balancing latency against throughput, through an interactive diagram that grows with each concept.Read →
- Design a Recommendation SystemBuild a large-scale recommendation system step by step. See how a two-stage retrieve-and-rank funnel picks the best few from millions, how two-tower embeddings and ANN generate candidates fast, how a heavy ranking model scores engagement, how a feature store stays consistent between training and serving, and how the feedback loop keeps recommendations fresh — through an interactive diagram that grows with each concept.Read →