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AI for FrontendIntermediate50 minutes
AI-Powered Smart Search
Design a semantic search experience using browser-side embeddings, vector similarity, and hybrid keyword fallback.
LLM-friendly summary
An intermediate AI-for-frontend problem about semantic search using browser-side embeddings, vector similarity, hybrid keyword fallback, and client-side index management.
Scenario
A documentation site with 2,000 pages needs search that understands intent, not just keywords. Users type natural language questions and expect relevant results even when the exact terms do not appear in the docs.
What you need to design
- 1Choose an embedding model that runs in the browser or via API.
- 2Design the indexing pipeline — when embeddings are computed, where they are stored.
- 3Implement vector similarity search with a scoring strategy.
- 4Combine semantic results with keyword matching for hybrid search.
- 5Handle cold start, index staleness, and large corpus scaling.
Concepts
EmbeddingsCosine SimilarityHybrid SearchIndexedDBTransformers.js
Skills
Search ArchitectureVector OperationsClient-Side Storage
What good solutions are evaluated on
- - Embedding strategy and model selection
- - Index storage and update architecture
- - Hybrid ranking and relevance quality
- - Scaling, cold start, and fallback handling
Ready to practice this yourself?
Open the interactive AlgoReason workspace to sketch the architecture, write notes, and submit for AI evaluation.