Guardrails And Safety medium
Your chat product streams tokens to the browser over SSE. Safety requires a moderation classifier on model output, and product requires that time to first token stays low. Which design best balances both?
- A Buffer the full response, classify it once, then send it; total completion time is unchanged so users see no real difference.
- B Classify sentence-sized windows in the stream path, accepting tens to hundreds of ms of delay, and cut the stream on a violation.
- C Classify asynchronously after delivery and flag violating conversations for review, keeping all latency out of the response path.
- D Skip output classification and rely on a strict input classifier, since a policy-compliant prompt yields policy-compliant output.
Answer: B. Classify sentence-sized windows in the stream path, accepting tens to hundreds of ms of delay, and cut the stream on a violation.
**Output classifiers in the stream path add latency**. Classifying small windows keeps that cost to tens or hundreds of milliseconds per chunk while still allowing you to stop harmful output. The downside is that a few tokens may reach the user before a cut. Async review (C) is tempting for its zero latency, but harmful content is already delivered, so it suits only low-risk analytics use cases. Full buffering (A) destroys time to first token.
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