Advanced

Embedding Services and Vector Retrieval

An embedding service converts inputs into searchable representations. Follow corpus indexing and query encoding, calculate vector payload and ingestion work, and distinguish retrieval quality from language-model generation.

Unlock the full curriculum — ₹2,000 / $25every concept + every answer · 6 months · no auto-renew

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 pipeline12 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.→