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Core
Token Streaming: SSE, Chunking, and Cancellation
Server-Sent Events is the default transport for one-way token streams, and the interesting problems start after you pick it: an output guardrail that buffers the whole response destroys the time-to-first-token you paid a GPU for, you cannot send an HTTP error status after the 200 has flushed, and a client disconnect must actually cancel the GPU work or you keep generating tokens nobody will read. AI, ML, and GenAI interviews probe it because 'we stream the tokens' is one sentence and shipping it correctly is a design round.
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PRACTICE THIS IN REAL QUESTIONS
System Design for AI in ProductionHow does token streaming work end to end, and what breaks when you put an output guardrail in front of it?→Coding & DSAConsume a streaming LLM response: SSE parsing, incremental output, cancellation, and partial JSON.→Coding & DSAImplement output guardrails that block off-topic answers and PII leakage, within a latency budget.→AI Security, Privacy & GovernanceA tool-using agent reads untrusted web content. How do you defend against prompt injection?→AI Security, Privacy & GovernanceDesign an evaluation and guardrail stack for an LLM feature: jailbreaks, toxicity, and hallucination.→Coding & DSAMaintain the running median of a number stream as values arrive.→
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