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This technique compresses LLM system prompts using learned "gist" tokens via knowledge distillation, significantly reducing inference latency and GPU requirements without sacrificing behavioral fidelity. By replacing thousands of natural language tokens with a compact set of embeddings, the method enables higher throughput for agent-based workloads while maintaining model performance.
Gisting: Compressing LLM Agent context to ↑ throughput and ↓ cost
System prompts can account for thousands of tokens per request. A longer prompt means slower, more expensive inference. This results in more GPUs required to accommodate the same traffic when serving a model on dedicated hardware. Our implementation of gisting, as first proposed in Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models,¹ allows us to reap the behavioral advantages of a long prompt at the cost of a short one. Through gisting, we cut down the Sidekick GraphQL agent’s system prompt from ~6,000 tokens to ~1,500 gist tokens (a 4:1 reduction) without losing prediction quality. We achieve this by learning…
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