Top 5 Embeddings Interview Questions

Browse 5 real-world technical and behavioral interview questions about Embeddings. Review scenarios, edge cases, and architectural best practices.

Related Topics:Rag Retrieval Evaluation Migration Multi tenancy

Your RAG system cannot answer a question whose answer is definitely in the documents. Where is the fault?

Almost always in retrieval rather than generation, and most often in chunking: if the answer was split across a boundary, or sits in a chunk whose surrounding context was stripped, no prompt can recover it. Evaluate the retrieval half separately, because it has a yes-or-no answer that needs no model to grade.

hardscenario

A better embedding model has come out. What does moving to it cost you?

Vectors from two models live in unrelated spaces, so there is no incremental path — you re-embed and reindex the whole corpus. The real cost is running both indexes side by side long enough to compare them on a gold set, keeping query and document models in step, and retuning thresholds calibrated on the old scores.

hardscenario

How do you isolate tenants in a shared vector index?

Choose between an index per tenant, per-tenant namespaces and one filtered index by weighing recall, blast radius and per-tenant fixed cost. Partitioning turns tenant selectivity from a search-quality problem into a routing decision, which is the argument most candidates miss.

harddesign

You need to reindex a ten-million-chunk corpus with a new embedding model. How do you do it without downtime?

Build a shadow index beside the live one, dual-write from a recorded change-feed watermark so it never falls behind, verify on reconciliation counts plus a gold set and shadowed production queries, then cut over by moving an alias and keep the old index writable long enough to roll back.

harddesign

What does a text embedding encode, and what does it fail to encode?

An embedding places text in a space where distance approximates the similarity its training data taught it. It does not encode relevance, and it represents exact identifiers, negation and numeric constraints so weakly that dense retrieval alone is unreliable wherever those decide the answer.

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