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Embedding Services and Vector Retrieval

Cosine compares vector direction; raw dot product also depends on length. Index size, ANN recall, evidence relevance and final-answer quality need separate checks.

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RELATED CONCEPTS

Tokens, Embeddings and Logits Model Metadata and Artifact Storage Dataset Lifecycle: Ingest, Shard and Retain Serving Benchmarks That Do Not Lie Evaluation Metrics and Data Splits

LESSONS THAT TEACH THIS

From Model to ServiceBuild retrieval as a budgeted pipeline22 min→From Model to ServiceChange the corpus without leaking old access12 min→

PRACTICE THIS IN REAL QUESTIONS

AI Infrastructure System DesignDesign a distributed search system with an LLM answer layer at 10,000 queries per second. Size both tiers and name the SLOs.→LLM Inference & ServingWhy do prefill and decode behave so differently, and why does that matter for the hardware you serve on?→LLM Inference & ServingWhat is the KV cache, and why does it keep growing while a request is being served?→LLM Inference & ServingWhat is the difference between static and continuous batching, and why did it change LLM serving?→LLM Inference & ServingHow does PagedAttention work, and what problem was it solving?→LLM Inference & ServingDefine TTFT, TPOT and goodput, and tell me how you would measure each one in production.→