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semantic search

AI, ML & GenAI interview questions tagged semantic search, across every topic.

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Concepts behind "semantic search"

The curriculum that explains the ideas these questions test.

Foundational
🧠 Foundations of LLMs & GenAI
EmbeddingsAn embedding maps text (or an image) to a dense vector so that semantic similarity turns into geometric closeness, similar meanings land near each other, measured by cosine similarity. Embeddings drive semantic search, retrieval, clustering, recommendation, and the vector index behind RAG. AI, ML, and GenAI engineer interviews probe them because they are the bridge between unstructured content and everything you can compute over it, and because their failure modes (domain mismatch, drift, the wrong similarity metric) quietly erode retrieval.
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