128Write an async batch caller for an LLM API: N requests, a concurrency cap, timeouts, and retries with backoff.▼mediumOpenAIAnthropicScale AI◆ premiumThe 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.Open full answer →
129Implement a minimal RAG pipeline end to end: embed and index a corpus, retrieve for a query, answer with citations.▼mediumAnthropicGleanPerplexity◆ premiumThe coding round version of the RAG question every candidate can describe in prose. Embed, index, retrieve, generate, cite. What separates a pass from a fail is the branch you write for when retrieval comes back with nothing worth quoting.Open full answer →
131Stream an LLM response to a browser. Handle cancellation, mid-stream failure, and a late guardrail.▼hardOpenAIAnthropicPerplexity◆ premiumEvery LLM product streams, and almost nobody can write the server. The interesting part is not the async generator, it is what happens when the user hits stop, the provider dies at token 90, or moderation flags text you have already put on the screen.Open full answer →
