rag

Recommend an approach to a CEO who needs a product decision. Explain what retrieval and fine-tuning each change, then connect that difference to freshness, reliability and cost.

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.

A larger context window changes what fits in a request. Decide whether to use it by checking corpus size, freshness, permissions and cost per successful query, including cache misses.

Everyone knows models make things up. The interviewer wants to hear why, which kinds of hallucination exist, and a layered set of product defences, without promising to eliminate it.

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.

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

A security design question that decides whether enterprise customers will sign. The answer has to make cross-tenant leakage impossible by construction, and then prove it with tests.

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.

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.