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Write an async batch caller for an LLM API: N requests, a concurrency cap, timeouts, and retries with backoff.

The most job-shaped coding screen in Applied AI: fan out N LLM calls without melting the rate limit or losing the batch to one bad request. The signal is in the retry policy, not the async syntax. Here is the version that passes.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

The most job-shaped coding screen in Applied AI: fan out N LLM calls without melting the rate limit or losing the batch to one bad request. The signal is in the retry policy, not the async syntax. Here is the version that passes.

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Next in this trackImplement a minimal RAG pipeline end to end: embed and index a corpus, retrieve for a query, answer with citations.Next in this trackWrite three chunkers (fixed-size with overlap, recursive separator, semantic) and defend when each wins.Next in this trackStream an LLM response to a browser. Handle cancellation, mid-stream failure, and a late guardrail.
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