TLDR Tech / Data
Context pruning: cut LLM tokens without losing quality
Context Pruning is the practice of selectively removing low-value tokens, sentences, or passages from an LLM's input to reduce cost, latency, and often improve output quality. It includes techniques such as token-level, sentence/chunk-level, attention-based, and dynamic layer-progressive pruning, and works best when paired with semantic caching.
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