Gisting
HoldTechniques
A prompt-compression technique that represents long LLM prompts with learned gist tokens.
Why it's here
Placed in Hold: 1 article(s) of evidence from 1 source(s), led by research-stage coverage, with 0 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 6InfoQ·9/3/2026researchShopify Introduces Gisting for LLM Prompt Compression
Shopify engineering introduced gisting, a technique that compresses long LLM system prompts into a smaller set of learned gist tokens. The approach aims to improve inference throughput and lower serving costs for large language model applications.