Generative AI & LLMs
Vector Databases & Embeddings
8 practice questions. Free questions open a full answer guide; the rest unlock with Pro.
- What is an embedding, and why do we use vector similarity instead of keyword matching to find related text?
- Why do we use a vector database with an approximate nearest-neighbor index instead of just computing the similarity to every stored vector?Go Pro
- You're choosing the vector index for a semantic search service and the options come down to an HNSW graph versus an IVF-based index. How do you reason about that choice and tune it for your recall and latency targets?Go Pro
- Your semantic-search service has grown to tens of millions of full-precision embeddings and the memory footprint and infra bill are getting painful. How would you reason about compressing the vectors to cut cost without quietly tanking search quality?Go Pro
- You're picking an embedding model and dimensionality for a new semantic-search system. Walk me through how you'd choose, including the similarity metric and how you'd handle the cost of storing and searching the vectors.Go Pro
- When you store embeddings in a vector database, how do you decide which similarity metric to use, like cosine similarity or dot product?Go Pro
- Your vector search returns good results in a small prototype but gets slow as the collection grows into the millions of vectors. What knobs do you have to scale it, and what trade-offs do they carry?Go Pro
- How do embeddings let a vector database do semantic search, and what determines whether two pieces of text come back as similar?Go Pro
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