Best practices for streaming LLM responses?

Has anyone solved the async streaming issue when summarizing long threads?

I tried an SSE server but it chokes on large docs.

Looking for best practices.

You can chunk text into windows of ~512 tokens and stream partial summaries.

Cache embeddings per-sentence to avoid recompute.

Use a faster model like GPT-4o-mini for streaming, then consolidate with a quality pass.

Also watch out for long code blocks—strip them or send them as attachments; they cause token explosion.

Using a shorter draft model for streaming helped reduce latency to under 200ms for first token.

For provenance tracking, I store character offsets when splitting sentences.

This lets you link highlights back to exact locations in posts.

Essential for verification!

We implemented a two-pass approach: fast streaming draft (200ms first token), then quality consolidation in parallel.

Cut total latency by 40% while maintaining accuracy.