Senior interview question
Your team switches to a newer embedding model with the same 384 dimensions and starts embedding incoming queries with it. Search quality collapses, but nothing errors. Explain what happened and how you'd run the migration.
Model answer
Each model arranges its own space, so matching dimensions only means the dot product can be computed. New-model queries against old-model documents give effectively random scores, with no error.
Migrate by re-embedding the whole collection, cutting queries and documents over together, and re-checking every threshold, since thresholds are model-specific. Even a preprocessing change invalidates stored vectors. Budget for it: generating embeddings costs money, and storage grows with dimension. At 4-byte floats, a million documents is about 1.5 GB at 384 dimensions against 12.3 GB at 3,072.
Practise the rest
17 Embeddings questions, 492 in all, each with a model answer. Free.
More Embeddings interview questions
- What is an embedding table, and what does it mean for two embeddings to be similar?
- People say 'embedding' for three different things. What are they, and why does the difference matter?
- Your vector database only supports dot-product search, but your embedding model's documentation says to compare vectors with cosine similarity. What do you do, and why does it work?
- You're building search over medicine leaflets with a small English embedding model, and you plan to reply 'no answer found' whenever the top cosine score is below 0.5. What risks do you raise before this ships?
- Embedding models return 1,536 or 3,072 numbers per text. What do those sizes mean?
- An input embedding table is too large for one device. Compare sharding it by vocabulary with sharding it by hidden dimension.