A Deterministic Prompt-Pruning Layer for Reducing Token Cost and Latency in LLM Systems

Short summary

As LLM conversations grow, prompts accumulate redundant tokens that increase cost and latency while degrading output quality. This article introduces a deterministic prompt-pruning layer designed to reduce token usage without breaking dependencies, supported by benchmarks and production-tested design. The approach targets engineers building cost-efficient LLM systems at scale.

• •Long context windows silently increase cost, latency, and output degradation
• •A deterministic prompt-pruning layer removes low-value tokens while preserving dependencies
• •Backed by real benchmarks and production-tested design

Generated with AI, which can make mistakes.