Ai Coding And Product Systems hard
Your completion prompt is assembled as [snippets from other files, re-sorted by relevance on every keystroke] + [current file up to the cursor] + [suffix]. Prefix-cache hit rate on the inference servers is 8%, and prefill dominates latency. What is the cause and the fix?
- A Cache keys include the code after the cursor; hash only the prefix so edits below the cursor stop invalidating cached entries.
- B KV cache eviction is too aggressive; reserve more GPU memory for cached prefixes so they survive longer between keystrokes.
- C Snippet retrieval is too slow; precompute embeddings for every file so retrieved context is ready before the request is sent.
- D Reordered snippets change the leading tokens; put stable content first and keep snippet order fixed across nearby keystrokes.
Answer: D. Reordered snippets change the leading tokens; put stable content first and keep snippet order fixed across nearby keystrokes.
Prefix caching only hits when the **leading tokens are identical**, so re-sorting snippets on every keystroke throws away the whole cache. Freezing order trades slightly less optimal snippet ranking for large prefill savings. Adding cache memory (B) is the tempting fix, but it cannot help when prefixes never repeat. It would only help if the hit rate were limited by eviction pressure.
One system design question a day, with spaced repetition so the ones you miss come back.
Get the appRelated questions
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