Deliberately mixing low-relevance same-domain items into context improved relevance accuracy by +0.077 - and six production patterns cut tokens 60-70%
A practitioner framework from a production dashboard (meetings/email/chat -> structured work items) names six token patterns: context stratification, fetch-once/process-locally, schema-contracted prompts, token-aware fallback chains, semantic caching, and inter-agent communication compression; together they cut cold-load latency from a 3.5-10.5 minute baseline to 61-116 seconds with an estimated 60-70% token reduction. The counterintuitive result comes from a controlled study of 2,420 trials across 11 model configurations using 661 anonymized workplace items: holding the prompt at a fixed ten items, replacing some high-relevance items with same-domain low-relevance items improved the model's relevance-score concordance on the target items versus an all-high-relevance prompt. The 50:50 signal/noise condition beat the 100%-relevant condition by +0.077 (95% CI [+0.056, +0.098], Cohen's d = 0.49, Holm-adjusted p < .001, n = 220) - which means aggressive relevance filtering of retrieved context can actively hurt discrimination.
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