LLMs don’t fail because they forget—they fail because they remember too much. As conversations grow, prompts accumulate redundant and low-value tokens, driving up cost and latency while silently degrading output quality. This article introduces a deterministic prompt-pruning layer that reduces token usage without breaking dependencies, backed by real benchmarks and production-tested design.
The post Long Context Isn’t Free — I Built a Safe Prompt-Pruning Layer That Makes LLM Systems Work appeared first on Towards Data Science.
