Shopify CTO Promotes Gisting to Compress LLM Prompts
Parakhin highlights gisting as a way to speed up models by compressing prompts into learned tokens.
Mikhail Parakhin, CTO at Shopify, posted that gisting compresses prompts before production use. He described it as zipping prompts to make them smaller and faster while improving results. Parakhin called it his favorite technique to tune models quickly without changing weights and linked to a Shopify engineering post on the method. The post states gisting compresses context into learned tokens to increase throughput and reduce cost while preserving quality. A reply from Alex Volkov asked about the skill or best way to apply it.
Gisting is the most underappreciated LLM technique right now. It is basically "zipping" your prompt before running in production - it stops being human-readable, but is much smaller, runs much faster: ~40% lower E2E latency, ~15% higher throughput and the results are BETTER!
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4 postsIf you don't use Gisting - you really should. My favorite technique to quickly tune your model and make it much faster without changing the weights themselves.
@MParakhin very veryinteresting, got a skill for that or a best way?
@altryne The easiest to try is probably just to go here: https://platform.toloka.ai - just upload your prompt. But it's not hard to set up yourself.
If you don't use Gisting - you really should. My favorite technique to quickly tune your model and make it much faster without changing the weights themselves.