Embeddings Interview Questions for 1 Year Experience

Vector databases power every RAG system, yet most candidates can't explain ANN algorithms or hybrid search. This fundamental question appears in 80% of AI engineering loops. Master dense vs sparse retrieval and when hybrid search wins.

Medium vector DB / embeddings trade-off question on dimensions vs cost/quality.

**Model selection** Benchmark retrieval on your corpus — smaller models cheaper if recall OK.

Latency, recall proxies, ingest lag, index size, filter rates, error budgets, and RAG downstream signals.

Encryption at rest/transit, tenant isolation, ACL on metadata filters, PII in embeddings, and audit logging.

efSearch, nprobe, over-fetch for filters, offline benchmarks, and continuous monitoring of retrieval quality.

Snapshot strategies, embedding model lineage, cross-region restore, and rebuild-from-source playbooks.

PCA, Matryoshka embeddings, learned compression, and recall impact when shrinking vector size.

Embedding geometry and index metric choice — normalization, MIPS, and provider defaults for text retrieval.

Embedding similarity, k-NN example banks, MMR diversity, and eval-driven example curation for few-shot prompts.

Model question on dense vector embeddings — semantic similarity, clustering, classification, dedup, and recommendation beyond vector search RAG.

RAG has become the foundational architecture for production GenAI applications at companies like Notion, Duolingo, and Morgan Stanley. Interviewers expect you to explain the full retrieval pipeline — not just define the acronym. Follow along to master what RAG is, when to use it over fine-tuning, and how to articulate trade-offs that separate junior from senior candidates.

Medium RAG interview on hard negatives for embedding/reranker quality.

**Fit** Nightly evals, backfill embeddings, document tagging — not interactive chat.

Field selection, normalization, ACL tags, temporal fields, and schema evolution without breaking filters.

Brute-force exact search thresholds, ANN recall trade-offs, and hybrid exact re-rank on shortlists.

Pricing dimensions: vectors stored, dimensions, QPS, namespaces, egress, and when self-host breaks even.

PostgreSQL + pgvector tuning: HNSW vs IVFFlat, connection pooling, vacuum, partitioning, and when to outgrow Postgres.

BM25 + vector fusion, RRF, weighted scores, rerankers, and same-chunk alignment pitfalls.

Bootstrap strategies, fallback retrieval, hybrid search defaults, and UX for new tenants or collections.

Read replicas, eventual consistency after upserts, quorum writes, and RAG staleness expectations.

ANN parameter tuning, rebuild windows, incremental indexing, and serving fresh vectors without missing SLOs.

Core ANN index families — graph-based HNSW, inverted-file IVF, and locality-sensitive hashing LSH — with recall, build cost, and memory trade-offs.

Medium RAG interview question on cost of embeddings at 100m documents — architecture, trade-offs, eval, and production patterns.