retrieval

People mostly find their own files by navigating, using search as a last resort. Keep navigation strong, bring related notes up while people write, and offer cited Q&A for when they can't remember where something is.

Retrieval quality decides most RAG products, and embeddings are where it starts. The interviewer wants the intuition, the failure modes, and the fixes a PM would push for.

Retrieval failures look like model failures to users. This question tests whether you can locate the failure precisely and choose fixes in the order that pays, rather than swapping models.

Many enterprise RAG failures happen before retrieval: the system never read the document correctly. The interviewer wants you to find the parsing problem and fix it at the right layer.

Sending the whole video with every question costs about a quarter of a million tokens. Index it once, retrieve the right minutes and keep the whole-video pass for one-off questions.

The model only knows what it was trained on. Internal policy belongs in your own dated retrieval, public news in search, and only the questions that need it should pay for search.

Two well-run teams published opposite defaults. Decide by repository size, how fast code changes, latency and whether customers will let you store embeddings of their code.

A RAG pipeline is the obvious first sketch. A strong answer starts with permissions instead, because one leaked salary document ends the product.

Stuffing every past conversation into context is expensive and still forgets. Decide what becomes a memory, how deletion cascades and how you test updates and forgetting.

A RAG answer can fail in the retriever or in the generator, and one accuracy score cannot say which. The interviewer wants two evals, a production loop and a launch bar tied to what a wrong answer costs.

Separate disabling access from completing erasure, and explain what evidence supports each part of the deletion promise.

Embeddings are infrastructure that customers store and rebuild around. The interviewer wants retrieval quality on real tasks, multilingual coverage, cost, and above all a plan for version changes, because switching embeddings means re-indexing everything.

RAG is the most common live-build topic. Strong candidates choose the simplest approach that works for 30 documents, make citations real, and test retrieval and answers separately.