cosine similarity
Applied AI interview questions tagged cosine similarity, across every topic.
2 questions · 1 unlocked for you
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 becomes geometric closeness, similar meanings land near each other, measured by cosine similarity. Embeddings power semantic search, retrieval, clustering, recommendation, and the vector index behind RAG. Applied-AI 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 degrade retrieval.🧠 Foundations of LLMs & GenAI
Core
Vector Search and ANN Indexes: HNSW, IVF, QuantizationWhy exact nearest-neighbor search does not scale, how HNSW, IVF, and product quantization trade recall for speed, and how to handle filtering and updates.🤖 Retrieval & AgentsSign in