Context Reduction Improves LLM Precision

When LLMs are given less accumulated context, even context that seems relevant, they often produce more accurate structured outputs.

In ontology generation, a “memoryless” approach that processed each requirement independently outperformed an approach that fed previous outputs back into the context. Reducing input context by ~60% actually improved the proportion of correctly modeled requirements.

The intuition: previous outputs become distractions. The model tries to maintain consistency with its earlier work rather than focusing on the current task. What feels like helpful context becomes noise that pulls attention away from what matters.

This pattern appears strongest when: (1) tasks can be decomposed into independent subtasks, (2) outputs need to be precise rather than creative, and (3) consistency between subtasks can be resolved through post-processing rather than in-context.

Related: 05-molecule—attention-mechanism-concept, 05-atom—context-window-limitations