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Designing a RAG QA system flashcards
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- Front
- In a permission-aware RAG system, where does the permission filter belong?
- Back
- Inside the retrieval query, using allowed groups stored as chunk metadata, so restricted text never reaches the prompt. Never ask the model to withhold something it was already shown.
- Front
- What should a RAG system do when the retrieved evidence is weak?
- Back
- Compare the best reranked score with a threshold tuned on unanswerable questions, and say it found nothing instead of generating a confident guess.
- Front
- Why might hybrid (keyword plus vector) search beat vector-only search?
- Back
- Vector search matches meaning but can blur exact strings such as error codes and product names; keyword search matches those exactly. The cost is a second index to keep in sync.
- Front
- Name degraded modes for a RAG system.
- Back
- Retriever down: keyword-only search. Reranker down: serve hybrid results. LLM down: fallback model or cited snippets only. Stale index: freshness warning, or refuse for sensitive sources.
- Front
- How do you estimate raw vector storage?
- Back
- Chunks × dimensions × bytes per value. Example: 2,000,000 × 1,024 × 4 bytes is about 8.2 GB before index overhead (illustrative numbers).
- Front
- In a typical RAG query, what dominates cost and what does a retriever cost?
- Back
- The LLM call dominates: retrieved chunks make up most of the input tokens. Retrieval and embedding are small by comparison, so cutting top-k is a first lever (and a quality trade).
- Front
- How do you close permission sync lag and cache leaks?
- Back
- Re-check the final few chunks against the source system before building the prompt, and key any answer cache on the user's permission set.