RAG Engineer

Identify the workload pattern where a traditional ANN-indexed vector database is the wrong tool, and a log-structured or batch-rebuild approach fits better.

Pick the right vector database for a startup with 2M documents, no DevOps headcount, and a one week launch deadline.

Which of these 2026 vector databases are credible at billion-vector scale? Select all that apply.

How does an HNSW index handle deletes and updates under a heavy write workload?

Sharding a vector index that does not fit on one node, how is it typically done, and what's the query-time cost?

A vendor's benchmark chart plots QPS against recall@10. What should you look for, and what claim should make you skeptical?

Define recall@10 for a vector index, and spell out exactly what 'ground truth' means in that definition.

Match each vector-compression scheme to its core mechanism.

Spot the conceptual error: 'We're using PQ instead of HNSW for our vector index because PQ is faster.'

Walk through how Product Quantization compresses a 1024-dim float32 vector down to 32 bytes, step by step.

PQ-based distance computation outruns full float32 distance even after decoding the vectors. Why?

At what scale does 'just put it in Postgres with pgvector' stop being the right answer?

pgvector ships two index types. Which should be your default reach in 2026, and why was it added later than the other?

Compare namespace-per-tenant against single-shared-index-with-tenant-filter for a B2B RAG product serving 5000 customers.

A team indexed embeddings under cosine but queried with Euclidean, and recall collapsed. Diagnose the cause.

What does Matryoshka representation learning buy a vector-database operator beyond regular embeddings?

Predict the shape of recall and latency on an IVF index of 10M vectors as nprobe sweeps from 1 to 50.

Spot the error in this candidate's description of IVF's two main parameters.

Fill in the IVF nlist rule of thumb.

Modern vector databases combine sparse (BM25) and dense vectors at the storage layer via two distinct patterns. Identify them.

Estimate the RAM cost of an HNSW index over 100M vectors at 1024 dimensions with M=32, including graph overhead.

As M grows in an HNSW index, four things move at once. Identify what climbs versus what slows.

Explain exactly what the ef_search knob on an HNSW index controls at query time.

Where does Hamming distance become the natural metric inside a vector database, and how is it implemented efficiently?

Explain what NVIDIA CAGRA buys you over CPU HNSW and the operational catch that decides whether you should reach for it.