context-management

To optimize conversation context and reduce token usage, you can prune irrelevant history, distill key insights from large tool outputs, and summarize data. This maintains AI performance and controls token costs during extended interactions.

The best way to distill large tool outputs is extracting essential findings into a concise summary before discarding raw data. This information distillation frees up context space for subsequent tasks while preserving critical insights.

Yes, you can use DCP slash commands and tool calls for dynamic context management. These commands support deduplication, supersede writes, and error purging to actively control conversation size and maintain AI efficiency.

You need context pruning when a research phase generates extensive tool outputs that threaten to exceed token limits. Pruning removes superseded information from conversation history, preventing performance degradation and unnecessary token consumption.

Yes, token budget management supports automatic deduplication and error purging. These features remove redundant information and purge errors from the conversation history, helping you stay within token limits during long-running interactions.