Prompt Compression

Prompt compression studies how to shorten instructions, documents, examples, and agent histories without losing information needed for a task. Methods include selecting salient sentences or tokens, rewriting text into structured forms, using learned encoders or soft tokens, and adapting compression to context structure and task needs. The goal is to lower inference cost, latency, or memory use in settings such as long-context question answering, retrieval, coding agents, and edge devices. A central tension is that fewer input tokens do not always mean lower total cost or reliable answers: omitted evidence, broken references, model-specific behavior, preprocessing overhead, and longer outputs can offset the gains.