context-compression

To compress LLM context during long coding sessions, apply anchored iterative summarization to incrementally summarize conversation history into structured sections like intent, file changes, and decisions, preserving essential technical details while reducing token usage.

The best way to reduce token usage without losing crucial information is using importance-based or task-boundary triggers to compress conversation history, applying regenerative full summaries at task boundaries to maintain a reliable artifact trail and clear readability.

Yes, you can use opaque compression to produce ultra-compact representations for maximum token savings when the LLM context window is exceeded, though this approach accepts reduced interpretability in exchange for shrinking the conversation history.

Anchored iterative summarization tracks artifacts by incrementally summarizing new content into structured sections including intent, file changes, decisions, and next steps, maintaining a reliable artifact trail throughout long-running AI sessions.

Fixed-threshold, sliding-window, importance-based, and task-boundary trigger strategies balance early context compression with information retention, allowing you to control when LLM conversation history is summarized based on token limits or workflow milestones.

You evaluate LLM summarization quality using probe-based tests including recall, artifact, continuation, and decision probes, which assess compression quality against tokens-per-task metrics to ensure essential details are preserved during context compression.