Mid-level interview questionAsked at Mercor · interview write-up
Rapid fire: how do RAG and fine-tuning relate, what do you do when content exceeds the context window, how do you keep conversation state, and how do you manage prompts?
Model answer
RAG and fine-tuning fill different gaps: RAG supplies changing or citable facts, fine-tuning shapes format, style or a narrow skill. Over the window: retrieve only the relevant chunks, or summarise in a map-reduce pass. Conversation state: keep recent turns verbatim, summarise older ones, and store durable facts outside the prompt. Prompts: version them like code, with templates and a regression suite. Treat retrieved text as data, never instructions, to limit prompt injection.
Practise the rest
12 Context selection questions, 492 in all, each with a model answer. Free.
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More Context selection interview questions
- “Our model has a 200K-token window, so let's just send the whole knowledge base.” What's wrong with that?
- You rank passages with reciprocal rank fusion of BM25 and embeddings, and keep the top one. The passage that answers the query never reaches the model. How?
- Your pipeline concatenates retrieved passages in database order. Does it matter where the most relevant one lands?
- A long support conversation no longer fits the context window. Compare the ways to shrink it, and how each one fails.
- Your retriever's top passage is usually on topic but often contradicts the right answer. What's going on, and what do you change?
- What is context engineering, and how is it different from prompt engineering?