Optimize vector semantic search for an assistant

Q: Optimize vector semantic search for an assistant

This question evaluates a candidate's competency in designing production-grade vector semantic search systems, including embedding model selection and training, ANN indexing and sharding, hybrid keyword-plus-semantic retrieval and reranking, caching and latency optimization, multi-tenant isolation, and freshness-aware update pipelines.

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Question

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Scenario

You own the vector semantic search layer for an AI assistant (e.g., Copilot). Users query across enterprise documents and/or product knowledge. Current issues:

Low recall for relevant passages
High latency and high cost
Stale results when documents update

Task

Design a plan to optimize the end-to-end vector search system.

Requirements

Support hybrid retrieval (keyword + semantic).
Multi-tenant isolation (enterprise customers).
Freshness within minutes of document updates.
Clear evaluation methodology.

What to cover

Embedding model choices and training
Indexing (ANN), sharding, filtering
Hybrid search + reranking
Caching and latency optimizations
Metrics and offline/online experiments

Optimize vector semantic search for an assistant

Overview

Scenario

Task

Requirements

What to cover

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