cosine similarity
AI, ML & GenAI interview questions tagged cosine similarity, across every topic.
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Concepts behind "cosine similarity"
The curriculum that explains the ideas these questions test.
Foundational
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.🧠 Foundations of LLMs & GenAI
Core
Vector Search and ANN IndexesVector search locates the embeddings closest to a query vector. Exact nearest-neighbor runs O(n) per query and will not scale, so production relies on Approximate Nearest Neighbor (ANN) indexes (HNSW, IVF, product quantization) that give up a little recall for enormous speedups. In practice the hard parts are the recall-vs-latency-vs-memory trade-off, metadata filtering, and coping with updates. AI, ML, and GenAI interviews test it because it is the engine beneath RAG and semantic search, and how you tune it directly sets retrieval quality and cost.🤖 Retrieval & AgentsSign in