CONTEXT PRUNING: CUT LLM TOKENS WITHOUT LOSING QUALITY

Summary from the TLDR Dev newsletter

Context pruning is a technique that removes low-value elements from an LLM's input, such as tokens or passages, to reduce costs and improve LLM output, often resulting in up to 20x compression and faster latency. It works by mitigating issues like "lost in the middle" effects common in long context windows, but must be applied carefully as it can negatively impact structured data or multi-turn dialogue.