Perplexity

Your team is comparing a vanilla LLM chatbot against a RAG chatbot. What is the single most important difference between them at query time?

Your team is tuning top-k for the retriever in a production RAG system. What is the strongest reason NOT to just push top-k to its highest possible value?

Design a production RAG system serving 10,000 concurrent users over a 100M-document corpus, with a P95 latency target of 2 seconds. Walk through the architecture and the bottleneck at each layer.

Fill in the blanks: query expansion / query rewriting in RAG and what it costs.

Predict the retrieval trace: how a multi-hop RAG agent answers a chained question that single-shot RAG would fail on.

Why does hybrid retrieval (BM25 plus dense vectors) typically outperform either alone in production RAG?

Describe a concrete, production runnable mechanism to detect hallucinations in a RAG answer (claims unsupported by the retrieved chunks), as the answer is generated or just after.

Spot the bug: a junior engineer's conversational RAG implementation produces nonsense retrievals on turn 3+. Find the error in the retrieval logic.

Select all techniques that meaningfully improve the reliability of source citations in a production RAG answer (chunks resolving back to real source documents).

You are tuning chunk size in a RAG pipeline. What is the core tradeoff between very small chunks (e.g. 128 tokens) and very large chunks (e.g. 2000 tokens)?