What are neural embeddings?
Dense vector representations produced by trained models that capture semantic relationships between texts.
Interview Questions: Neural Embeddings
Use these to explain model choice with production maturity.
Why use them in RAG?
They improve retrieval when user language differs from document wording, especially for paraphrase-heavy queries.
How do you choose an embedding model?
Run corpus-specific evals and compare quality, cost, latency, privacy, dimension compatibility, and migration risk.
What happens when you change models?
You usually need to re-embed the corpus, create a new vector index, run evals, and release with rollback.
Why use hybrid retrieval?
Dense retrieval handles meaning; sparse retrieval protects exact IDs, codes, names, and rare terms.
What metadata should be stored?
Embedding model, dimension, index version, chunk id, source id, access label, effective date, and chunker version.
What are common failure modes?
Paraphrase misses, exact-term misses, stale chunks, restricted data retrieval, dimension mismatch, drift, and cost spikes.
How do you evaluate embeddings?
Use golden queries, expected chunks, Recall@k, MRR, gold rank, citation support, and answer quality checks.
How do privacy requirements affect model choice?
Sensitive corpora may require local models, private hosted deployments, approved providers, or strict data residency controls.
What is the senior answer to "which embedding is best?"
Best depends on corpus, query patterns, constraints, eval results, and operating model. There is no universal winner.