5 exercises — practise answering Retrieval-Augmented Generation Architect interview questions in professional technical English.
0 / 5 completed
1 / 5
The interviewer asks: "Our RAG system retrieves plausible-looking but factually wrong chunks for a subset of queries. How would you debug this?" Which answer best demonstrates Retrieval-Augmented Generation Architect expertise?
Option B is strongest because it isolates retrieval from generation with proper metrics, then walks through concrete, verifiable root causes including chunking, domain mismatch, and hybrid search gaps. Option A masks the symptom and increases noise and cost without fixing the underlying retrieval error. Option C is not universally true — bigger embedding models can underperform on domain-specific text without fine-tuning. Option D incorrectly claims retrieval is undebuggable and misdirects effort toward the wrong layer.
2 / 5
The interviewer asks: "How do you choose a chunking strategy for a RAG pipeline over a mixed corpus of API docs, PDFs, and Slack threads?" Which answer best demonstrates Retrieval-Augmented Generation Architect expertise?
Option B is strongest because it tailors chunking to each content type's actual structure and preserves provenance metadata for citation and verification. Option A ignores that structured and conversational content have different natural boundaries than prose. Option C is not feasible — embedding models do not perform chunk-boundary detection; that is upstream preprocessing. Option D understates chunking's impact, which is widely recognized as one of the highest-leverage decisions in RAG quality.
3 / 5
The interviewer asks: "When would you use hybrid search versus pure dense vector retrieval, and how do you combine their scores?" Which answer best demonstrates Retrieval-Augmented Generation Architect expertise?
Option B is strongest because it explains the concrete failure mode of pure dense retrieval, recommends reciprocal rank fusion or cross-encoder re-ranking over naive score combination, and stresses per-corpus empirical validation. Option A overstates dense retrieval's superiority; exact-match queries are a well-known dense-retrieval weakness. Option C inverts the actual tradeoff — hybrid search benefits scale with corpus diversity, not shrink. Option D uses a fragile combination method that does not generalize across corpora with different score distributions.
4 / 5
The interviewer asks: "How would you design a RAG evaluation pipeline that catches regressions before they reach production?" Which answer best demonstrates Retrieval-Augmented Generation Architect expertise?
Option B is strongest because it separates retrieval and generation evaluation, uses real query distributions, gates on regression thresholds, and explicitly guards against aggregate metrics hiding segment-specific failures. Option A is unsystematic and will miss regressions outside the spot-checked queries. Option C uses production feedback as the sole signal, which is slow, biased toward vocal users, and provides no pre-deployment gate. Option D ignores that a generator can only be as good as the context it receives — retrieval failures manifest as generation failures without isolating the true cause.
5 / 5
The interviewer asks: "How do you handle RAG over frequently updated or time-sensitive documents, like pricing pages or policy documents?" Which answer best demonstrates Retrieval-Augmented Generation Architect expertise?
Option B is strongest because it ties freshness to explicit SLAs per document category, uses incremental indexing to bound both cost and staleness, and correctly routes truly real-time facts to structured lookups instead of embeddings. Option A applies one schedule regardless of volatility, which is either wasteful or too stale depending on the category. Option C relies on the model to infer staleness from prose, which is unreliable and does not prevent retrieving outdated chunks. Option D over-corrects by excluding a whole category from RAG rather than architecting appropriate freshness handling.