Interview guides
Deep study guides for production AI interviews — RAG, agents, MCP, evaluation, and system design — each paired with curated questions.
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14 guides in RAG · 105 curated questions
RAGQuery rewriting in RAG: contextualize follow-ups, then multi-query with RRF, HyDE, step-back, or sub-questions. Know the drift and latency interviewers probe.
RAGRAG model ignoring retrieved context? Prove the gold chunk reached the prompt, find whether memory or a wrong chunk won, then fix the layer that failed.
RAGBudget a RAG request stage by stage. Generation drives latency, retrieved tokens drive cost. Tune top-k, rerank depth, ANN dials, and caching against recall.
RAGA prompt line is not an ACL. Bound the store before the model sees another tenant's chunks, and judge the retrieve response, not the answer.
RAGChoose the model by retrieval task and corpus. Pair the query encoder with the document encoder. Swap the function and you rebuild the index.
RAGRetrievers own recall; cross-encoders reorder a shortlist. Cite BEIR carefully, label vendor cost claims, and diagnose missing recall before you add latency.
RAGSeparate BM25 from dense retrieval, then fuse with reciprocal rank fusion. Cite the datasets where each wins. Do not average raw scores with cosine.
RAGInterviewers want chunk size and overlap trade-offs, not a magic default. Cover recursive vs fixed, tokens vs characters, and how you evaluate.
RAGMetadata filters can silently exclude the chunk that answers the question. Pre-filter vs post-filter, ACL traps, over-filtering, and interview debugging tips.
RAGWrong RAG answers are usually retrieval, filters, or ranking, not the model. Split the failure, then fix chunks, top-k, rerank, and citations.
RAGAdvanced RAG interviews test agentic retrieval loops, GraphRAG trade-offs, corrective and self-RAG patterns, and when complexity actually improves answer quality.
RAGLearn how to debug RAG regressions by separating retrieval failures from generation drift, building slice-aware eval pipelines, and choosing metrics that actually predict user pain.
RAGProduction RAG interviews test whether you can ship retrieval systems that stay grounded under real traffic, tenant isolation, and document churn—not whether you can draw a naive retrieve-then-generate diagram.
Everything you need to ace RAG interviews — from fundamentals and hallucination mitigation to enterprise pipeline design.