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Browse 9 real-world technical and behavioral interview questions about Rag. Review scenarios, edge cases, and architectural best practices.
Source the questions from real users and from randomly sampled documents rather than from the team, label each with a quotable answer string anchored to a document rather than a chunk id so the set survives re-chunking, and hold part of it back so you are not tuning against your own measurement.
Almost always in retrieval rather than generation, and most often in chunking: if the answer was split across a boundary, or sits in a chunk whose surrounding context was stripped, no prompt can recover it. Evaluate the retrieval half separately, because it has a yes-or-no answer that needs no model to grade.
Vectors from two models live in unrelated spaces, so there is no incremental path — you re-embed and reindex the whole corpus. The real cost is running both indexes side by side long enough to compare them on a gold set, keeping query and document models in step, and retuning thresholds calibrated on the old scores.
Debug a RAG system with confident wrong answers by separating retrieval failure from generation failure. Check whether the gold passage was retrieved, then measure recall@k, reranking quality, faithfulness and abstention on a labelled eval set. It also connects retrieval evaluation to the point an interviewer is testing.
Chunk boundaries decide what can ever be retrieved, so you derive them from the questions the corpus must answer and from the document's own structure, then confirm the choice with retrieval recall on a gold set rather than by eye.
Route each format to a parser that respects its structure, treat OCR output as text with a confidence score rather than as truth, convert tables into rows that carry their headers, and record a section path and stable anchor per chunk so every retrieved passage can be cited back to a place in the original.
Detect change at the source rather than by re-crawling everything, upsert at document granularity so moved chunk boundaries do not orphan rows, propagate deletions as explicit tombstones the query filter respects, and publish the source-to-searchable lag as a measured SLA.
An embedding places text in a space where distance approximates the similarity its training data taught it. It does not encode relevance, and it represents exact identifiers, negation and numeric constraints so weakly that dense retrieval alone is unreliable wherever those decide the answer.
Each chunk needs its source document and version, its section path, an anchor precise enough to open, effective and ingestion dates, and the permission set that governed the original — because filtering, citation, freshness and access control are all served from metadata rather than from the vector.